Image Splicing Forgery Approachs: A Review and Future Direction

Ernawati, Muji; Ernawan, Ferda; Abbker, Yagoub; Fakhreldin, Mohammad; Adi, Prajanto Wahyu

doi:10.1109/icicos56336.2022.9930543

Cited by 2 publications

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“…In today's digital era, the widespread availability of powerful image editing tools has led to a signi cant increase in the occurrence of image tampering, raising concerns about the authenticity and integrity of digital visual content. Image tampering refers to any unauthorized alteration or manipulation of an image, which can range from subtle modi cations to more sophisticated techniques such as cloning, splicing [1][2][3], and retouching [4]. Detecting such tampering has become a pressing challenge for various domains, including forensics, journalism, and image authentication.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Local Binary Pattern(LBP) Variants for Image Tamper Detection

Kaur

2023

Preprint

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The proliferation of image tampering in the digital age poses a significant challenge to the authenticity and integrity of visual content. This study presents an approach for detecting image tampering using the Local Binary Pattern (LBP) techniques in conjunction with Convolutional Neural Network (CNN). LBP is a powerful image texture descriptor. The LBP method is employed to extract robust and discriminative features by capturing local texture and intensity patterns from tampered images. These features are then input into a CNN architecture, which is trained using 5-fold cross-validation to ensure generalization and prevent overfitting. A comprehensive benchmark image dataset CASIA-2.0 comprising of 7,541 authentic and 5,124 tampered images is utilized to evaluate the proposed method, and performance evaluation metrics, including accuracy, and confusion matrix, are employed to assess the effectiveness of the system. Experimental results demonstrate the efficiency of the proposed approach over existing state-of-the-art methods, achieving high accuracy in detecting image tampering. A comparative analysis of four types of LBP variants is presented in this work. With circular LBP, Rotation-Invariant LBP, Default, and Uniform LBP we achieved an accuracy of 68%, 72%, 84%, and 96% respectively. This research has significant implications in various domains, including forensic investigations, journalism, and image integrity verification, as it addresses the challenges posed by image tampering, enhancing trust and confidence in digital visual content by ensuring its authenticity and reliability.

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Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Local Binary Pattern(LBP) Variants for Image Tamper Detection

Kaur

2023

Preprint

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Detecting Post Editing of Multimedia Images using Transfer Learning and Fine Tuning

Jonker,

Jelstrup,

Meng

et al. 2024

ACM Trans. Multimedia Comput. Commun. Appl.

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In the domain of general image forgery detection, a myriad of different classification solutions have been developed to distinguish a “tampered” image from a “pristine” image. In this work, we aim to develop a new method to tackle the problem of binary image forgery detection. Our approach builds upon the extensive training that state-of-the-art image classification models have undergone on regular images from the ImageNet dataset, and transfers that knowledge to the image forgery detection space. By leveraging transfer learning and fine tuning, we can fit state-of-the-art image classification models to the forgery detection task. We train the models on a diverse and evenly distributed image forgery dataset. With five models—EfficientNetB0, VGG16, Xception, ResNet50V2, and NASNet-Large—we transferred and adapted pre-trained knowledge from ImageNet to the forgery detection task. Each model was fitted, fine-tuned, and evaluated according to a set of performance metrics. Our evaluation demonstrated the efficacy of large-scale image classification models—paired with transfer learning and fine tuning—at detecting image forgeries. When pitted against a previously unseen dataset, the best-performing model of EfficientNetB0 could achieve an accuracy rate of nearly 89.7%.

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Image Splicing Forgery Approachs: A Review and Future Direction

Cited by 2 publications

References 0 publications

A Comparative Analysis of Local Binary Pattern(LBP) Variants for Image Tamper Detection

A Comparative Analysis of Local Binary Pattern(LBP) Variants for Image Tamper Detection

Detecting Post Editing of Multimedia Images using Transfer Learning and Fine Tuning

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