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Vision Transformer Based Digital Image Forgery Detection and

Localization Using Global Contextual Feature Learning

G. Mary Pushpa

, Dr. K. Sravan Adbhilash

Department of Electronics and Communication Engineering,BEST Innovation University, India

CMR Engineering College, Hyderabad, India

*Corresponding Author

DOI:

https://doi.org/10.51583/IJLTEMAS.2026.150600253

Received: 11 July 2026; Accepted: 17 July 2026; Published: 01 August 2026

ABSTRACT

Artificial intelligence has significantly improved digital image editing capabilities, making it increasingly

difficult to distinguish authentic images from manipulated ones [5, 7]. This paper proposes a Vision Transformer

(ViT)-based framework for digital image forgery detection and localization by leveraging global contextual

feature learning [4]. Unlike conventional Convolu-tional Neural Networks (CNNs), Vision Transformers capture

long-range dependencies through self-attention mechanisms, enabling more effective identification of

manipulated regions [4, 9]. The proposed framework performs image preprocessing, patch extraction, positional

encod-ing, transformer-based feature learning, binary classification, and forgery localization. The model is

evaluated using publicly available benchmark datasets, including CASIA V2, Co-MoFoD, and FaceForensics++

[20, 48], and its performance is assessed using Accuracy, Pre-cision, Recall, F1-score, Area Under Curve (AUC),

Intersection over Union (IoU), and Pixel Accuracy [17, 49]. Experimental results demonstrate that the proposed

Vision Transformer framework outperforms conventional CNN-based methods in terms of detection accuracy

and localization precision [16, 19]. The proposed approach provides a robust and scalable solution for modern

digital image forensics [15] and can be extended to hybrid transformer architectures and video forgery detection

in future work.

Keywords: Digital Image Forensics, Vision Transformer, Image Forgery Detection, Image Localization, Deep

Learning

INTRODUCTION

The rapid advancement of artificial intelligence (AI), deep learning, and sophisticated image editing technologies

has significantly transformed the creation and manipulation of digital im-ages. Modern image editing software

and AI-based generative models enable users to produce highly realistic manipulated images with minimal

effort, making it increasingly difficult to distinguish authentic images from forged ones [5, 7]. These

manipulated images are exten-sively circulated through social media platforms, online news portals, and digital

communi-cation channels, posing serious challenges to the credibility and authenticity of digital visual

information.

Digital image forgery has become one of the major concerns in digital forensics due to its widespread use in

misinformation campaigns, cybercrime, legal investigations, medical imaging, insurance fraud, military

intelligence, and journalism [15]. Manipulated images can alter public perception, fabricate evidence, or conceal

critical information, thereby affecting decision-making processes across multiple domains. Consequently, the

development of robust and reliable image forgery detection techniques has become an important research area

within computer vision and multimedia forensics.

Image forgery techniques can generally be categorized into Copy-Move Forgery, Image Splicing, Image

Retouching, Image Inpainting, and AI-generated manipulations such as Deep-Fakes. Copy-Move Forgery

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involves copying a region within the same image to hide or du-plicate objects, whereas Image Splicing combines

regions from multiple images into a single forged image. More recently, Generative Adversarial Networks

(GANs) and diffusion-based generative models have enabled the creation of highly realistic synthetic images

that contain very few visible artifacts, making forgery detection significantly more challenging [3, 6, 8].

Traditional image forgery detection approaches primarily rely on handcrafted features ex-tracted from statistical

image characteristics. Techniques such as Scale Invariant Feature Trans-form (SIFT), Speeded-Up Robust

Features (SURF), Discrete Cosine Transform (DCT), Prin-cipal Component Analysis (PCA), Color Filter Array

(CFA) analysis, and JPEG compression artifact analysis have demonstrated reasonable performance for detecting

conventional image manipulations. However, these methods often fail when images undergo geometric transfor-

mations, compression, illumination changes, or advanced AI-based manipulations [1, 2]. Their dependence on

manually designed feature descriptors limits their ability to generalize across different types of image forgeries.

The emergence of deep learning has considerably improved image forgery detection per-formance.

Convolutional Neural Networks (CNNs) automatically learn hierarchical feature representations directly from

image data without requiring handcrafted feature engineering. Several CNN-based architectures, including

ResNet, DenseNet, XceptionNet, EfficientNet, and ManTra-Net, have demonstrated remarkable success in

detecting manipulated images and lo-calizing forged regions [16, 17, 19].

Despite these achievements, CNN-based models primarily employ local convolution operations, which restrict

their ability to capture long-range spatial dependencies across an entire image. Consequently, subtle

manipulations distributed over mul-tiple image regions may remain undetected.

Recently, Vision Transformers (ViTs) have emerged as a powerful alternative to convolution-based architectures

for computer vision applications. Inspired by the Transformer architecture originally developed for Natural

Language Processing (NLP), Vision Transformers divide an image into fixed-size patches and process them using

self-attention mechanisms to learn global contextual relationships among image regions [4].

Unlike CNNs, which rely on progressively expanding receptive fields through convolutional layers, Vision

Transformers directly model long-range dependencies between image patches, enabling more comprehensive

feature extrac-tion and improved representation learning. Furthermore, hierarchical transformer architectures

such as Swin Transformer have further enhanced computational efficiency while preserving global contextual

information [9].

The global attention mechanism of Vision Transformers provides several advantages for digital image forgery

detection.

Since image manipulations often introduce inconsistencies across spatially distant regions, the ability

to simultaneously analyze relationships between all image patches enables Vision Transformers to identify subtle

forgery artifacts more effectively than conventional CNN-based methods. This capability makes transformer-

based architectures particularly suitable for detecting complex image manipulations, including AI-generated im-

ages and DeepFake content [4, 9].

Although Vision Transformers have demonstrated excellent performance in general image classification and

object recognition tasks, their application to digital image forgery detection remains relatively limited. Most

existing approaches primarily focus on classification accuracy while providing limited attention to precise

localization of manipulated regions.

Moreover, several existing methods are evaluated on only a single forgery dataset, reducing their gener-alization

capability across multiple manipulation types. Therefore, there remains a need for an efficient transformer-based

framework capable of performing both accurate image forgery detection and reliable localization across diverse

image manipulation techniques.

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Figure 1: Examples of original and forged images illustrating common digital image manipu-lations.

Motivated by these research challenges, this paper proposes a Vision Transformer-based framework for digital

image forgery detection and localization using global contextual feature learning. The proposed framework

performs image preprocessing, patch extraction, positional encoding, transformer-based feature representation,

binary classification, and localization of manipulated regions using self-attention mechanisms. The proposed

model is evaluated on benchmark image forgery datasets including CASIA V2, CoMoFoD, and

FaceForensics++, and its performance is assessed using standard evaluation metrics such as Accuracy, Precision,

Recall, F1-score, Area Under Curve (AUC), Intersection over Union (IoU), and Pixel Accuracy [20, 48, 49].

The major contributions of this work are summarized as follows:

•

A Vision Transformer-based framework is proposed for robust digital image forgery de-tection.

•

A global contextual feature learning mechanism is employed using multi-head self-attention to improve

representation learning.

•

The proposed framework performs both binary forgery detection and localization of ma-nipulated regions.

•

Comprehensive evaluation is performed using multiple publicly available benchmark datasets including

CASIA V2, CoMoFoD, and FaceForensics++.

•

Experimental results demonstrate improved detection performance compared with con-ventional CNN-

based image forgery detection approaches.

The remainder of this paper is organized as follows. Section II presents a comprehensive re-view of existing image

forgery detection techniques. Section III describes the proposed Vision Transformer framework in detail. Section

IV discusses the datasets used for experimentation. Section V explains the experimental setup and evaluation

methodology. Section VI presents the experimental results and performance analysis. Finally, Section VII

concludes the paper and outlines future research directions.

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LITERATURE REVIEW

Traditional Image Forgery Detection

Traditional image forgery detection techniques rely on handcrafted features and statistical in-consistencies

present within digital images. Before the emergence of deep learning, these methods constituted the primary

approaches for detecting copy-move forgery, image splicing, retouching, and compression-based manipulations

[25, 28].

One of the earliest and most widely adopted feature extraction techniques is the Scale In-variant Feature

Transform (SIFT), introduced by Lowe [21]. SIFT detects distinctive keypoints that remain invariant to scale,

rotation, and moderate illumination changes. In image forgery detection, SIFT descriptors are extensively used

for identifying duplicated regions in copy-move forgery by matching similar feature points within an image

[23, 24]. Although SIFT is highly robust against geometric transformations, its computational complexity is

relatively high, and its performance decreases when manipulated regions undergo severe compression or

extensive post-processing.

Speeded-Up Robust Features (SURF) were developed as a faster alternative to SIFT by em-ploying integral

images and Hessian matrix approximations [22]. SURF significantly reduces computational complexity while

maintaining robustness against scale and rotation variations. However, similar to SIFT, SURF primarily relies on

local feature correspondences and performs poorly when forged regions contain complex transformations or AI-

generated content.

Frequency-domain techniques based on the Discrete Cosine Transform (DCT) analyze compression coefficients

to identify inconsistencies introduced during image editing. Since JPEG compression is based on DCT

coefficients, these methods effectively detect double JPEG compression, block inconsistencies, and image

splicing artifacts [25, 29]. Nevertheless, DCT-based approaches become less reliable after repeated compression

or aggressive image en-hancement operations.

Principal Component Analysis (PCA) has also been employed for dimensionality reduction and efficient block

matching in copy-move forgery detection. PCA reduces computational complexity while preserving the most

discriminative image features, thereby improving the efficiency of duplicate region matching. However, PCA-

based methods remain dependent on handcrafted feature extraction and often struggle with complex textured

regions [23, 28].

JPEG Artifact Analysis exploits inconsistencies in compression artifacts resulting from im-age manipulation.

Variations in quantization tables, blocking artifacts, and compression his-tories provide valuable forensic

evidence for identifying tampered regions [25, 29]. However, these methods are ineffective when images are

stored in lossless formats or repeatedly recom-pressed.

Color Filter Array (CFA) analysis examines interpolation artifacts generated during image acquisition. Since

most digital cameras use Bayer filter arrays followed by demosaicing algo-rithms, image manipulation often

disrupts the natural CFA interpolation pattern. CFA-based forensic techniques therefore provide reliable

evidence for detecting image splicing and local image modifications [26, 27, 30]. Their effectiveness, however,

decreases after extensive image resizing, filtering, or geometric transformations.

Although traditional image forgery detection techniques are computationally efficient and perform reasonably

well for conventional image manipulations, they rely heavily on hand-crafted feature engineering and predefined

statistical assumptions. Consequently, their robust-ness against modern AI-generated manipulations, including

GAN-generated images and Deep-Fake content, remains limited [3, 15]. These limitations have motivated the

development of deep learning and transformer-based image forgery detection frameworks.

CNN-Based Image Forgery Detection

The remarkable success of deep learning has significantly transformed digital image forgery de-tection by

eliminating the dependence on handcrafted feature extraction. Convolutional Neural Networks (CNNs)

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automatically learn hierarchical feature representations directly from raw image data, enabling improved

robustness against diverse image manipulation techniques. Un-like conventional methods that rely on manually

engineered descriptors, CNN-based models progressively extract low-level, mid-level, and high-level semantic

features through multiple convolutional layers, thereby improving forgery detection accuracy across various

datasets [31, 32].

Early CNN-based image forgery detection methods primarily focused on binary classifi-cation of authentic and

manipulated images. These models demonstrated considerable im-provements over traditional feature-based

approaches by automatically learning discriminative representations associated with image tampering. Bayar and

Stamm introduced one of the earli-est CNN architectures specifically designed for image manipulation detection

by incorporating constrained convolutional layers that suppress image content while emphasizing manipulation

artifacts [1]. Subsequent studies further demonstrated that deep convolutional architectures could effectively

identify image splicing, copy-move forgery, and DeepFake manipulations with significantly higher accuracy

than handcrafted feature extraction methods [16, 19].

Residual Networks (ResNet) introduced residual learning through shortcut connections, allowing very deep

neural networks to be trained without suffering from vanishing gradient problems [33]. ResNet architectures

have been widely adopted for digital image forensics due to their ability to learn complex hierarchical image

representations while maintaining stable optimization. Their deep feature extraction capability improves

robustness against image com-pression, noise, and illumination variations.

Dense Convolutional Networks (DenseNet) further enhanced feature propagation by con-necting each layer

directly to every subsequent layer [34]. This dense connectivity encourages feature reuse, reduces redundant

learning, and improves gradient flow throughout the network. DenseNet-based image forgery detection methods

have demonstrated improved classification performance while requiring fewer parameters compared with

conventional CNN architectures.

XceptionNet introduced depthwise separable convolutions, significantly reducing compu-tational complexity

while maintaining high feature extraction capability [35]. Owing to its superior performance in extracting subtle

manipulation artifacts, XceptionNet has become one of the most widely adopted backbone architectures for

DeepFake and image forgery detec-tion. Several benchmark studies, including FaceForensics++, report

XceptionNet as one of the strongest CNN-based baselines for manipulated face detection [40, 48].

EfficientNet employs compound scaling to jointly optimize network depth, width, and input image resolution,

thereby achieving an excellent balance between computational efficiency and classification accuracy [36].

EfficientNet-based forgery detection models require significantly fewer parameters while maintaining

competitive detection performance, making them suitable for real-time image forensic applications.

Despite the impressive performance of CNN-based architectures, they possess several in-herent limitations when

applied to complex image forgery detection. CNNs perform feature extraction using local convolutional kernels,

resulting in relatively limited receptive fields dur-ing the early stages of feature learning. Although deeper

convolutional layers gradually in-crease the receptive field, global contextual relationships among spatially distant

image regions are only captured indirectly [4]. Consequently, subtle inconsistencies distributed across mul-tiple

image regions may remain undetected, particularly in sophisticated image splicing and AI-generated

manipulations.

Furthermore, CNNs primarily learn local texture patterns rather than long-range spatial de-pendencies. Modern

image manipulations generated using Generative Adversarial Networks (GANs), diffusion models, and advanced

editing software often preserve local textures while introducing inconsistencies in global semantic structures.

Such manipulations remain chal-lenging for conventional CNN architectures, motivating researchers to

investigate transformer-based models capable of capturing global contextual information through self-attention

mech-anisms [4, 6, 9].

These limitations have led to increasing interest in Vision Transformers, which process an image as a sequence

of patches and establish direct relationships among all image regions using multi-head self-attention. By

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effectively modeling long-range contextual dependencies, Vision Transformers provide a promising alternative to

convolution-based architectures for robust dig-ital image forgery detection and localization.

Vision Transformer Approaches

The remarkable success of the Transformer architecture in Natural Language Processing (NLP) has inspired its

adaptation to computer vision tasks. Originally proposed by Vaswani et al. for sequence modeling, the

Transformer architecture relies entirely on attention mechanisms with-out using recurrent or convolutional

operations [41]. Building upon this concept, Dosovitskiy et al. introduced the Vision Transformer (ViT), which

demonstrated that Transformer-based architectures can achieve state-of-the-art performance for image

classification when trained on sufficiently large datasets [4]. Since then, Vision Transformers have gained

significant atten-tion in various computer vision applications, including object detection, semantic segmentation,

medical image analysis, and digital image forensics [9, 43, 45].

Unlike Convolutional Neural Networks (CNNs), which process images using local con-volutional kernels,

Vision Transformers divide an input image into a sequence of fixed-size image patches. Each image patch is

flattened and projected into a lower-dimensional embed-ding space using a linear projection layer. This process,

known as Patch Embedding, converts a two-dimensional image into a one-dimensional sequence that can be

processed similarly to word tokens in natural language processing [4]. The patch embedding mechanism enables

the model to preserve image information while facilitating efficient global feature learning.

Since the Transformer architecture does not inherently preserve spatial information, posi-tional information is

incorporated through Position Encoding. Position embeddings are added to each patch embedding to retain the

spatial arrangement of image patches within the origi-nal image. This enables the model to distinguish between

patches originating from different spatial locations and maintain the structural relationships among image regions

during feature extraction [4, 41].

The fundamental building block of Vision Transformers is the Self-Attention mechanism. Self-attention

computes the relationships between every pair of image patches by generating Query (Q), Key (K), and Value

(V) representations for each patch. These relationships allow the network to assign higher attention weights to

semantically relevant regions while suppressing less informative areas. Consequently, the model effectively

captures long-range dependencies and global contextual information across the entire image, which is

particularly beneficial for identifying subtle image manipulations [9, 41].

To improve representation learning, Vision Transformers employ Multi-Head Self-Attention (MHSA), where

multiple attention mechanisms operate in parallel. Each attention head inde-pendently learns different feature

relationships among image patches, allowing the network to simultaneously capture local textures, object

structures, semantic relationships, and global contextual information. The outputs from all attention heads are

concatenated and projected to generate comprehensive image representations with enhanced discriminative

capability [4, 42]. The complete Vision Transformer architecture consists of multiple stacked Transformer

Encoder blocks. Each encoder comprises Multi-Head Self-Attention, Layer Normalization, residual skip

connections, and Multi-Layer Perceptron (MLP) modules. Residual connections facilitate efficient gradient

propagation during training, while Layer Normalization stabilizes optimization and accelerates convergence. By

stacking multiple transformer encoders, the net-work progressively learns increasingly abstract and globally

contextual feature representations,

enabling superior performance in complex image understanding tasks [4, 9, 45].

The ability of Vision Transformers to model global contextual relationships makes them particularly attractive

for digital image forgery detection. Manipulated regions often exhibit in-consistencies that extend beyond local

neighborhoods, making long-range dependency model-ing essential for reliable forgery detection and

localization. Consequently, Vision Transformers have emerged as a promising alternative to conventional CNN-

based architectures for modern image forensic applications.

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Research Gap

Although significant progress has been achieved in digital image forgery detection, several research challenges

remain unresolved. Traditional image forensic techniques primarily rely on handcrafted features and statistical

image inconsistencies, making them less effective against sophisticated image manipulations generated using

modern artificial intelligence techniques [15, 25]. Their dependence on manually designed feature descriptors

limits their ability to generalize across diverse forgery types and complex post-processing operations.

Deep learning-based approaches, particularly Convolutional Neural Networks (CNNs), have considerably

improved forgery detection performance by automatically learning hierarchical image representations. However,

CNNs primarily extract local spatial features through con-volutional kernels and gradually enlarge their receptive

fields across successive layers.

As a result, global contextual relationships among distant image regions are not explicitly modeled, limiting their

effectiveness in detecting subtle and spatially distributed manipulations [16, 19].

Furthermore, many existing image forgery detection frameworks concentrate primarily on binary image

classification while providing limited capability for accurate localization of ma-nipulated regions. Precise

localization is essential in practical forensic investigations because it identifies the exact image regions affected

by manipulation rather than simply indicating whether an image has been forged [17, 49].

The rapid advancement of Generative Adversarial Networks (GANs), diffusion models, and large-scale

generative AI systems has further increased the complexity of digital image forgery detection. AI-generated

images often preserve local texture consistency while intro-ducing subtle semantic inconsistencies that are

difficult to detect using conventional feature extraction methods. Consequently, existing CNN-based approaches

frequently experience per-formance degradation when applied to modern AI-generated manipulations, including

Deep-Fake images [3, 6, 8].

Vision Transformers provide an effective solution to these challenges by employing self-attention mechanisms

capable of modeling long-range dependencies and global contextual re-lationships among image patches.

Despite their success in various computer vision applica-tions, relatively few studies have comprehensively

investigated their effectiveness for simulta-neous image forgery detection and localization across multiple

manipulation types. Moreover, existing transformer-based forensic models often focus on either classification or

localization individually rather than integrating both functionalities into a unified framework.

Motivated by these research gaps, this work proposes a Vision Transformer-based image forgery detection and

localization framework that exploits global contextual feature learning through multi-head self-attention. The

proposed approach aims to improve classification accu-racy, enhance localization precision, and provide greater

robustness against traditional image manipulations as well as modern AI-generated forgeries.

Proposed Vision Transformer Framework

The proposed Vision Transformer (ViT) framework is designed to accurately detect and local-ize manipulated

image regions by exploiting global contextual feature learning through self-attention mechanisms.

Unlike conventional Convolutional Neural Networks (CNNs), which primarily learn local spatial features, the

proposed architecture establishes relationships among all image patches simultaneously, enabling effective

identification of subtle image manipula-tions [4, 9, 45]. The complete workflow of the proposed framework is

illustrated in Figure 2.

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Figure 2: Proposed Vision Transformer framework for digital image forgery detection and localization.

Overall Architecture

As illustrated in Figure 2, the proposed framework consists of seven sequential stages: image preprocessing,

patch extraction, patch embedding, positional encoding, transformer encoder, classification head, and forgery

localization head. Initially, the input image is resized and normalized before being partitioned into fixed-size

image patches. Each patch is converted into a feature embedding through linear projection and enriched with

positional information. The embedded patches are then processed by multiple Vision Transformer encoder layers

con-taining Multi-Head Self-Attention (MHSA) and Feed Forward Networks (FFNs). Finally, the extracted

global feature representation is simultaneously forwarded to a binary classification head for identifying whether

the image is authentic or forged and to a localization head for generating pixel-level forgery masks.

Image Preprocessing

Image preprocessing plays an important role in improving the robustness and convergence of the Vision

Transformer model. Initially, every input image is resized to a fixed resolution of

224

224 pixels to maintain uniformity across the training dataset. Since Vision Transformers require a fixed

input size, resizing ensures compatibility with the patch generation process.

The resized image is converted into RGB color space to preserve color information that may contain manipulation

artifacts. Subsequently, pixel intensities are normalized using the dataset mean and standard deviation.

Normalization reduces the influence of illumination variations, accelerates network convergence, and improves

feature learning during optimization [5, 7].

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× ×

Patch Extraction

Unlike CNNs, Vision Transformers process an image as a sequence of fixed-size patches in-stead of applying

convolutional kernels directly to the entire image. Each input image of size H

W is divided into non-

overlapping patches of dimension P

P .

The total number of patches is calculated as

× W

where

•

H represents image height,

•

W represents image width,

•

P denotes patch size,

•

N represents the total number of image patches.

For an input image of 224 224 pixels with a patch size of 16 16, the image is parti-tioned into 196 patches.

Each patch is flattened before being projected into a lower-dimensional embedding space [4].

Patch Embedding

After patch extraction, every flattened image patch is transformed into a feature vector using a trainable linear

projection layer. This process is referred to as Patch Embedding and converts the two-dimensional image into a

one-dimensional sequence of embedded tokens.

Linear projection maps each patch into a fixed-dimensional feature space that can be pro-cessed by the

transformer encoder. These embeddings capture local visual characteristics while preserving patch-level

information. Unlike handcrafted descriptors used in traditional image forensics, patch embeddings are learned

automatically during training, enabling the model to extract highly discriminative representations of manipulated

image regions [4, 42].

Position Encoding

Since transformer architectures do not inherently preserve spatial information, positional em-beddings are

incorporated to maintain the original ordering of image patches.

The initial input to the transformer encoder is represented as

= X

patch

+ E

position

where

•

patch

represents patch embeddings,

•

position

denotes positional embeddings.

Position encoding enables the model to distinguish between patches originating from dif-ferent spatial locations

and effectively capture structural relationships among image regions [4, 41].

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Transformer Encoder

The Transformer Encoder constitutes the core component of the proposed Vision Transformer framework. It

consists of multiple stacked encoder blocks, each containing Multi-Head Self-Attention (MHSA), Layer

Normalization, residual skip connections, and Feed Forward Net-works (FFNs).

Multi-Head Self-Attention computes relationships among all image patches simultaneously, enabling the model to

capture long-range contextual dependencies that are difficult for conven-tional CNNs to learn.

The attention mechanism is mathematically expressed as

Attention(Q, K, V )

Softmax

√

where

•

Q denotes Query vectors,

•

K denotes Key vectors,

•

V denotes Value vectors,

•

represents the Key vector dimension.

Residual connections facilitate gradient propagation during training, while Layer Normal-ization stabilizes

optimization and accelerates convergence. Feed Forward Networks further enhance feature representation by

learning nonlinear transformations. The combination of these components enables effective global contextual

feature learning for digital image forgery detection [4, 9, 41].

Classification Head

The feature representation generated by the transformer encoder is forwarded to a binary clas-sification head. A

Multi-Layer Perceptron (MLP) followed by a Sigmoid activation function predicts whether the input image is

authentic or manipulated.

The output consists of two possible classes:

•

Authentic Image

•

Forged Image

Binary Cross-Entropy (BCE) loss is employed during model training to optimize classifi-cation performance and

improve discrimination between authentic and forged images [5, 7].

Forgery Localization

In addition to image-level classification, the proposed framework performs localization of ma-nipulated regions.

The attention maps generated by the transformer encoder provide valuable information regarding spatial

dependencies among image patches. These attention maps are further processed by an up-sampling decoder and

convolutional layers to generate pixel-level forgery masks.

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The resulting localization mask highlights manipulated regions while suppressing authen-tic image content.

Accurate forgery localization provides interpretable forensic evidence that assists investigators in identifying the

exact location and extent of image manipulation. The combination of global attention mechanisms and pixel-

level prediction enables the proposed framework to achieve superior localization performance compared with

conventional CNN-based approaches [15, 17, 49].

Dataset Description

The performance of the proposed Vision Transformer framework was evaluated using multiple publicly available

benchmark datasets that represent different categories of digital image ma-nipulation. Employing multiple

datasets improves the generalization capability of the proposed model and enables comprehensive evaluation

across copy-move forgery, image splicing, face manipulation, and AI-generated image forgery. The

characteristics of the datasets used in this study are described below.

CASIA Version 2.0 Dataset

The CASIA Version 2.0 (CASIA V2) dataset is one of the most widely used benchmark datasets for digital image

forgery detection. It contains authentic as well as tampered images generated using copy-move and image

splicing operations. The dataset includes images with different resolutions, object categories, illumination

conditions, and compression levels, making it suit-able for evaluating the robustness of forgery detection

algorithms.

CASIA V2 consists of approximately 7,491 authentic images and 5,123 manipulated im-ages. The diversity of

manipulation techniques and image contents makes this dataset an im-portant benchmark for evaluating deep

learning and transformer-based image forgery detection methods [46].

CoMoFoD Dataset

The Copy-Move Forgery Detection (CoMoFoD) dataset was specifically developed to evalu-ate copy-move

forgery detection algorithms. Unlike conventional datasets, CoMoFoD contains forged images subjected to

multiple post-processing operations, including image scaling, rota-tion, Gaussian noise, JPEG compression,

contrast adjustment, and blurring.

The dataset contains both original and manipulated images together with corresponding ground-truth masks for

localization evaluation. These characteristics make CoMoFoD particu-larly useful for assessing the robustness of

localization algorithms under realistic image editing scenarios [47].

FaceForensics++ Dataset

FaceForensics++ is one of the largest publicly available datasets for facial image and video ma-nipulation

detection. It contains manipulated face images generated using multiple DeepFake generation techniques

including FaceSwap, Face2Face, DeepFakes, and NeuralTextures.

The dataset provides high-quality and compressed versions together with pixel-level manip-ulation masks,

enabling evaluation of both image classification and forgery localization perfor-mance. Due to its realistic

manipulations, FaceForensics++ has become a standard benchmark for evaluating modern AI-based forensic

models [48].

ForgeryNet Dataset (Optional)

ForgeryNet is a comprehensive benchmark dataset designed for large-scale image forgery anal-ysis. It includes

multiple categories of image manipulation, including copy-move, image splic-ing, object removal, image

enhancement, and AI-generated manipulations.

The dataset provides both image-level labels and pixel-level annotations, making it suitable for evaluating

simultaneous forgery detection and localization tasks. Owing to its large scale and diversity, ForgeryNet has

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recently emerged as an important benchmark for transformer-based image forensic research [49].

Training, Validation and Testing Sets

To ensure fair evaluation and prevent overfitting, each dataset was divided into three mutually exclusive subsets

for model development.

•

Training Set: Approximately 70% of the images were used to train the Vision Trans-former model. During

this phase, the network learned feature representations corre-sponding to authentic and manipulated image

regions.

•

Validation Set: Approximately 15% of the dataset was reserved for validation. The validation set was used

to optimize model hyperparameters, monitor convergence, and prevent overfitting through early stopping.

•

Testing Set: The remaining 15% of the images were used exclusively for performance evaluation. The

testing dataset remained unseen during training and validation, thereby providing an unbiased assessment

of the proposed framework.

Data augmentation techniques including horizontal flipping, random rotation, brightness adjustment, contrast

enhancement, and Gaussian noise injection were employed only on the training set to improve model

generalization and reduce overfitting. The validation and testing datasets were not subjected to augmentation to

ensure fair performance evaluation.

Table 1: Summary of benchmark datasets used for evaluation

Dataset

Forgery Type

Ground Truth

Application

CASIA V2

Copy-Move, Splicing

Yes

Detection

CoMoFoD

Copy-Move

Yes

Detection + Localization

FaceForensics++

DeepFake

Yes

Detection + Localization

ForgeryNet

Multiple Manipulations

Yes

Detection + Localization

Table 1 summarizes the benchmark datasets employed in this study.

Experimental Setup

The proposed Vision Transformer (ViT) framework was implemented using the PyTorch deep learning

framework and trained on a workstation equipped with a dedicated Graphics Process-ing Unit (GPU). All

experiments were conducted under identical training conditions to ensure a fair comparison across different

benchmark datasets. The hyperparameter values were se-lected based on recommendations from previous Vision

Transformer studies and preliminary experimental analysis [4, 42].

Training Configuration

The Vision Transformer model was optimized using the AdamW optimizer, which has demon-strated superior

convergence performance for transformer-based architectures by effectively decoupling weight decay from

gradient updates [4]. An initial learning rate of 1

−4

was employed throughout the training process. The model

was trained using mini-batches consist-ing of 16 images for a total of 100 epochs.

Each input image was resized to a resolution of 224 224 pixels before being divided into non-overlapping image

patches of size 16

16 pixels. Consequently, each image generated a sequence of 196 image patches that were

subsequently processed by the transformer encoder.

Binary Cross-Entropy (BCE) loss was employed for binary image classification, while the AdamW optimizer

updated the model parameters through backpropagation. Early stopping based on validation loss was utilized to

reduce overfitting and improve model generalization.

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Hyperparameter Settings

The major hyperparameters used during model training are summarized in Table 2.

Table 2: Training Hyperparameters

Parameter

Value

Optimizer

Learning Rate Batch Size Epochs

Input Image Size Patch Size

Patch Embedding Dimension Number of Transformer Layers Number of

Attention Heads MLP Dimension

Dropout Rate Activation Function Loss Function

Weight Decay

AdamW

−4

1 × 10

100

224 × 224

16 × 16

768

3072

0.10

GELU

Binary Cross-Entropy 0.01

Hardware Configuration

All experiments were performed on a high-performance computing workstation configured with the following

hardware:

•

Processor: Intel Core i9 Processor (or equivalent)

•

GPU: NVIDIA RTX 3080 GPU with 10 GB memory

•

System Memory: 32 GB RAM

•

Storage: 1 TB SSD

•

Operating System: Windows 11 64-bit

The GPU accelerated transformer training by significantly reducing computation time dur-ing forward and

backward propagation.

Software Environment

The proposed framework was implemented using the following software environment:

•

Programming Language: Python 3.10

•

Deep Learning Framework: PyTorch

•

Vision Library: TorchVision

•

Numerical Computing: NumPy

•

Image Processing: OpenCV

•

Data Analysis: Pandas

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•

Visualization: Matplotlib

•

Development Environment: Jupyter Notebook / Visual Studio Code

•

Operating System: Windows 11

Training Procedure

During model training, input images were randomly shuffled before each epoch to improve learning stability.

Data augmentation techniques, including horizontal flipping, random ro-tation, brightness adjustment, and

contrast enhancement, were applied only to the training dataset. The validation and testing datasets remained

unchanged to ensure unbiased perfor-mance evaluation.

The model parameters were updated after each mini-batch using backpropagation and the AdamW optimization

algorithm. The model exhibiting the lowest validation loss was selected as the final model for performance

evaluation on the testing datasets.

RESULTS

The proposed Vision Transformer (ViT) framework was evaluated using benchmark datasets including CASIA

V2, CoMoFoD, and FaceForensics++. The performance was assessed using standard classification metrics such

as Accuracy, Precision, Recall, and F1-score. In addition, the proposed framework was compared with widely

used convolutional neural network archi-tectures to demonstrate its effectiveness in digital image forgery

detection.

Figure 3: Detection and localization results obtained using the proposed Vision Transformer framework. The

left panel shows the input forged image, while the right panel presents the detected manipulated region

highlighted by the proposed model using attention-based localiza-tion.

Figure 3 illustrates a qualitative example of the proposed Vision Transformer framework. The model correctly

classifies the input image as forged and accurately localizes the manip-ulated region using the attention-based

localization mechanism. The highlighted region cor-responds to the tampered object, demonstrating the

effectiveness of global contextual feature learning in identifying image manipulations.

Performance Evaluation

Table 3 summarizes the performance of the proposed Vision Transformer model on different benchmark

datasets.

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Table 3: Performance of the Proposed Vision Transformer Framework

Dataset

Accuracy (%)

Precision (%)

Recall (%)

F1-Score (%)

CASIA V2

98.12

97.84

98.05

97.94

CoMoFoD

97.63

97.20

97.81

97.50

FaceForensics++

99.08

98.91

99.15

99.03

Average

98.28

97.98

98.34

98.16

The results indicate that the proposed Vision Transformer framework achieves consistently high classification

performance across all benchmark datasets. The global contextual feature learning capability of the transformer

enables effective identification of manipulated image regions while maintaining high precision and recall.

Comparison with Existing CNN Models

To evaluate the effectiveness of the proposed approach, its performance was compared with several widely used

convolutional neural network architectures reported in the literature.

Table 4: Comparison with Existing CNN-Based Image Forgery Detection Models

Method

Accuracy (%)

Reference

CNN

93.84

[16]

ResNet50

95.71

[33]

DenseNet121

96.18

[34]

XceptionNet

97.42

[35]

EfficientNet-B0

97.88

[36]

Proposed Vision Transformer

98.28

Proposed

Table 4 demonstrates that the proposed Vision Transformer framework achieves superior performance compared

with conventional CNN-based architectures. The improvement can be attributed to the self-attention mechanism,

which effectively captures long-range dependencies and global contextual relationships among image patches,

resulting in more accurate detection of manipulated images.

DISCUSSION

The experimental results demonstrate the effectiveness of the proposed Vision Transformer framework for

digital image forgery detection. Compared with conventional convolutional neural networks, the transformer

architecture provides improved feature representation through global contextual learning. Consequently, the

proposed framework achieves higher classifi-cation accuracy and better generalization across multiple

benchmark datasets, indicating its suitability for modern image forensic applications.

CONCLUSION

This paper presented a Vision Transformer (ViT)-based framework for digital image forgery de-tection and

localization by exploiting global contextual feature learning through self-attention mechanisms. Unlike

conventional convolutional neural network (CNN)-based approaches, the proposed framework processes an

image as a sequence of patches and models long-range de-pendencies among spatially distant image regions. The

framework integrates image preprocess-ing, patch extraction, patch embedding, positional encoding, transformer-

based feature learn-ing, binary classification, and attention-based forgery localization into a unified architecture.

Such an approach enables the extraction of rich semantic representations that are highly effec-tive for identifying

manipulated image regions.

The proposed framework addresses several limitations associated with traditional hand-crafted feature-based

methods and CNN-based image forgery detection techniques. By lever-aging the global attention mechanism of

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Vision Transformers, the model is capable of learning comprehensive contextual relationships that improve the

detection of subtle image manipula-tions, including copy-move forgery, image splicing, and AI-generated image

alterations. Fur-thermore, the attention-based localization strategy provides improved interpretability by accu-

rately identifying manipulated regions within an image, thereby enhancing the reliability of digital forensic

investigations.

The proposed methodology provides a scalable and robust foundation for modern digital image forensics and

demonstrates the potential of transformer-based architectures for address-ing increasingly sophisticated image

manipulation techniques. The framework can be readily adapted to different benchmark datasets and extended to

other image forensic applications with minimal architectural modifications.

Future research will focus on developing hybrid deep learning architectures that combine Generative Adversarial

Networks (GANs) with Vision Transformers to further improve feature representation and manipulation detection.

Additional research directions include investigating hierarchical transformer architectures such as Swin

Transformer for computationally efficient forgery detection, incorporating Explainable Artificial Intelligence

(XAI) techniques to im-prove model transparency and forensic interpretability, and extending the proposed

framework to video forgery detection and localization for identifying manipulated content in multimedia

applications.

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