Digital image forgery: taxonomy, techniques, and tools–a comprehensive study

Chaitra, B. R.; Reddy, P. Vijaya Bhaskar

doi:10.1007/s13198-022-01829-5

Cited by 3 publications

(6 citation statements)

References 73 publications

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“…Copy-move forgery is a digital image forgery where the manipulator duplicates and pastes part of an image within the same image to cover or replicate certain objects or regions [4]. This technique manipulates the visual content so that it may deceive mere viewers.…”

Section: Figure 1 Taxonomy Of Digital Image Forgery Techniquesmentioning

confidence: 99%

“…With this forgery technique, a specific part, object, or element from one image is spliced into another, creating a false impression or manipulating the overall image. This process involves the manipulator cutting out the region of interest from one image and moving onto another to create misleading, fabricated visual content [4,11]. This technique usually combines people or objects in a way that is not originally present in the scene.…”

Section: Figure 1 Taxonomy Of Digital Image Forgery Techniquesmentioning

confidence: 99%

“…Retouching forgery, also known as image retouching, is a type of image forgery technique that involves altering or enhancing the appearance of an image through different digital image editing techniques [11,12]. Unlike some digital image forgery techniques with deceptive or malicious intentions, retouching forgery may not always be the case [4]. It can be used for legitimate purposes, such as improving aesthetics or correcting imperfections in an image.…”

Section: Figure 1 Taxonomy Of Digital Image Forgery Techniquesmentioning

confidence: 99%

“…Digital image forgery covers a variety of techniques, such as copy-move forgery, splicing forgery, retouching forgery, and many others, which change the image properties, features, or content [2]. Perpetrators of such acts make use of a variety of widely available editing tools such as Adobe Photoshop, GNU (GIMP) (GNU's Not Unix Image Manipulation Program), and many more, which are equipped with advanced feature that makes image tampering very easy [4]. These tools, which are freely accessible, give perpetrators the courage to create forgeries with remarkable precision, which further complicates the challenge of distinguishing between authentic and manipulated content.…”

Section: Introductionmentioning

confidence: 99%

“…The rise in digital image forgery and deepfake technology calls for a robust image forgery detection system as the potential consequences of deceptive media manipulation and misinformation continue to become more profound [5]. The combination of AI tools and the manipulation of digital images adds complexity to the challenges faced by security and image forensics [4]. As a branch of digital forensics, image forensics focuses on analyzing and authenticating digital images.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.

NFOR,

Armand,

Kim

2024

Preprint

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In the face of the escalating use of image editing tools and the rapid progress of AI technology, the demand for robust image forgery detection techniques has become more pressing. This study introduces a comprehensive approach to image forgery detection, which integrates image metadata analysis and Error Level Analysis (ELA) with deep learning, specifically Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). Using transfer learning, we evaluated seven pre-trained models alongside a custom innovative CNN model using the CASIAV2 dataset for binary classification of authentic and forged images. Metadata was extracted from all the images, and unique feature images were generated and used as inputs for all models by exploiting the differences between compressed and original images through ELA. Among the pre-trained models, VGG16, Xception, ResNet101, and MobileNetV2 demonstrated superior detection accuracy compared to the state-of-the-art models. However, our custom model surpassed six others, achieving an accuracy score of 99.08% with efficient training parameters. This study's results indicate significant advancements in image forgery detection, which has practical applications in forensic tools, automated detection systems, and educational purposes. An enhanced accuracy can benefit industries reliant on image authenticity, improve digital security protocols, protect intellectual property, and influence legal standards. Additionally, the methodology could extend to other domains like document and video authentication.

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Section: Figure 1 Taxonomy Of Digital Image Forgery Techniquesmentioning

confidence: 99%

Section: Figure 1 Taxonomy Of Digital Image Forgery Techniquesmentioning

confidence: 99%

Section: Figure 1 Taxonomy Of Digital Image Forgery Techniquesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.

NFOR,

Armand,

Kim

2024

Preprint

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Combating medical image tampering using deep transfer learning

Latif,

Brahim,

Mohammad

et al. 2024

AIP Conference Proceedings

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Copy-move forgery detection algorithm based on binarized statistical image features and principal component analysis

Bensaad,

Loukhaoukha,

Sadoudi

et al. 2024

J. Electron. Imag.

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The most common form of image forgery is copy-move, which arises when an image region is duplicated and pasted onto another region of the same image. An effective algorithm for copy-move forgery detection based on binarized statistical image features (BSIF) and principal component analysis (PCA) is presented. Initially, the suspicious image is converted to grayscale and is subsequently partitioned into overlapping blocks. Feature vectors are extracted from these blocks using BSIF, followed by dimensionality reduction using PCA. Next, as a precursor to the matching step, the feature vectors are sorted lexicographically. Additionally, a morphological opening operation is applied to eliminate outliers. This algorithm offers not just forgery detection but also the ability to localize and identify duplicated regions. The proposed algorithm was assessed using three datasets: CoMoFoD, GRIP, and UNIPA. The experimental results show that this algorithm is fast and has high accuracy for forgery detection and localization. Moreover, it has high robustness under various postprocessing operations, such as brightness, contrast adjustments, and blurring. Furthermore, the proposed algorithm outperforms some recent approaches in overall performance.

show abstract

Digital image forgery: taxonomy, techniques, and tools–a comprehensive study

Cited by 3 publications

References 73 publications

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.

A Holistic Approach to Image Forensics: Integrating Image Metadata Analysis and ELA with CNN and MLP for Image Forgery.

Combating medical image tampering using deep transfer learning

Copy-move forgery detection algorithm based on binarized statistical image features and principal component analysis

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