Book chapter
Refined GAN-Based Attack Against Image Splicing Detection and Localization Algorithms
Adversarial Multimedia Forensics, pp 93-123
15 Nov 2023
Abstract
Recently, researchers have developed new image splicing detection and localization algorithms by leveraging deep learning and contrastive learning techniques. These algorithms utilize Siamese neural networks to detect and localize spliced contents by identifying inconsistencies in an image’s forensic traces. At the same time, deep learning has also enabled the researchers to develop new types of anti-forensic attack that can fool forensic algorithms. Among these attacks, the generative adversarial network (GAN) based approach has demonstrated superior performance in attacking forensic algorithms, including Siamese neural network based forensic algorithms. However, GAN-based attacks can sometimes still fail to fool these forensic algorithms due to the lack of direct control over the distribution of forensic traces in spliced images. In this chapter, we propose a new attack that refine the GAN-based attack on individual spliced images. Our attack achieves this by directly reducing the difference of forensic traces within the spliced images. Through a series of experiments, we demonstrate the capability of our attack to convincingly deceive Siamese neural network based image splicing detection and localization algorithms. The proposed attack significantly improves attack performance and successfully fool the targeted splicing detection and localization algorithms when the GAN-based attack fails.
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Details
- Title
- Refined GAN-Based Attack Against Image Splicing Detection and Localization Algorithms
- Creators
- Shengbang Fang - Drexel UniversityMatthew C. Stamm - Drexel University
- Publication Details
- Adversarial Multimedia Forensics, pp 93-123
- Series
- Advances in Information Security
- Publisher
- Springer Nature Switzerland; Cham
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Scopus ID
- 2-s2.0-85188917590
- Other Identifier
- 991021860736404721