# Transferable Adversarial Attack on Image Tampering Localization

arXiv: https://arxiv.org/abs/2309.10243 · HTML: https://mlsanity.com/paper/2309.10243/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 34 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [PSCC-Net: Progressive Spatio-Channel Correlation Network for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2103.10596/) (2021)
- [TruFor: Leveraging all-round clues for trustworthy image forgery detection and localization](https://mlsanity.com/paper/2212.10957/) (2022)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)
- [Image Manipulation Detection by Multi-View Multi-Scale Supervision](https://mlsanity.com/paper/2104.06832/) (2021)
- [Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization](https://mlsanity.com/paper/2108.12947/) (2021)
- [Exposing Fake Images with Forensic Similarity Graphs](https://mlsanity.com/paper/1912.02861/) (2019)
- [On the Transferability of Adversarial Examples Against CNN-Based Image Forensics](https://mlsanity.com/paper/1811.01629/) (2018)
- [A Counter-Forensic Method for CNN-Based Camera Model Identification](https://mlsanity.com/paper/1805.02131/) (2018)
- [Attacking Image Splicing Detection and Localization Algorithms Using Synthetic Traces](https://mlsanity.com/paper/2211.12314/) (2022)

## Built on by (most cited first, 2 of 2)

- [Image Forgery Localization with State Space Models](https://mlsanity.com/paper/2412.11214/) (2024)
- [Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack](https://mlsanity.com/paper/2407.07041/) (2024)

## Abstract (arXiv)

It is significant to evaluate the security of existing digital image tampering localization algorithms in real-world applications. In this paper, we propose an adversarial attack scheme to reveal the reliability of such tampering localizers, which would be fooled and fail to predict altered regions correctly. Specifically, the adversarial examples based on optimization and gradient are implemented for white/black-box attacks. Correspondingly, the adversarial example is optimized via reverse gradient propagation, and the perturbation is added adaptively in the direction of gradient rising. The black-box attack is achieved by relying on the transferability of such adversarial examples to different localizers. Extensive evaluations verify that the proposed attack sharply reduces the localization accuracy while preserving high visual quality of the attacked images.