# A Survey of Machine Learning Techniques in Adversarial Image Forensics

arXiv: https://arxiv.org/abs/2010.09680 · HTML: https://mlsanity.com/paper/2010.09680/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Level Up the Deepfake Detection: a Method to Effectively Discriminate Images Generated by GAN Architectures and Diffusion Models](https://mlsanity.com/paper/2303.00608/) (2023)
- [Demystifying the Transferability of Adversarial Attacks in Computer Networks](https://mlsanity.com/paper/2110.04488/) (2021)
- [Compacting Deep Neural Networks for Internet of Things: Methods and Applications](https://mlsanity.com/paper/2103.11083/) (2021)
- [An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework](https://mlsanity.com/paper/2204.13172/) (2022)
- [Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization](https://mlsanity.com/paper/2209.04930/) (2022)
- [Transferring Visual Attributes from Natural Language to Verified Image Generation](https://mlsanity.com/paper/2305.15026/) (2023)
- [Do Not Deceive Your Employer with a Virtual Background: A Video Conferencing Manipulation-Detection System](https://mlsanity.com/paper/2106.15130/) (2021)
- [FuncFooler: A Practical Black-box Attack Against Learning-based Binary Code Similarity Detection Methods](https://mlsanity.com/paper/2208.14191/) (2022)
- [Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks](https://mlsanity.com/paper/2209.12195/) (2022)
- [Spritz-PS: Validation of Synthetic Face Images Using a Large Dataset of Printed Documents](https://mlsanity.com/paper/2304.02982/) (2023)
- [Prompt-Consistency Image Generation (PCIG): A Unified Framework Integrating LLMs, Knowledge Graphs, and Controllable Diffusion Models](https://mlsanity.com/paper/2406.16333/) (2024)
- [VIPPrint: A Large Scale Dataset of Printed and Scanned Images for Synthetic Face Images Detection and Source Linking](https://mlsanity.com/paper/2102.06792/) (2021)
- [Real or Virtual: A Video Conferencing Background Manipulation-Detection System](https://mlsanity.com/paper/2204.11853/) (2022)
- [PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques](https://mlsanity.com/paper/2211.08864/) (2022)

## Abstract (arXiv)

Image forensic plays a crucial role in both criminal investigations (e.g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups) and civil litigation (e.g., defamation). Increasingly, machine learning approaches are also utilized in image forensics. However, there are also a number of limitations and vulnerabilities associated with machine learning-based approaches, for example how to detect adversarial (image) examples, with real-world consequences (e.g., inadmissible evidence, or wrongful conviction). Therefore, with a focus on image forensics, this paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios.