# FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction

arXiv: https://arxiv.org/abs/2006.07533 · HTML: https://mlsanity.com/paper/2006.07533/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## Built on by (most cited first, 30 of 33)

- [Spatiotemporal Inconsistency Learning for DeepFake Video Detection](https://mlsanity.com/paper/2109.01860/) (2021)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection](https://mlsanity.com/paper/2305.06321/) (2023)
- [Personalization as a Shortcut for Few-Shot Backdoor Attack against Text-to-Image Diffusion Models](https://mlsanity.com/paper/2305.10701/) (2023)
- [FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs](https://mlsanity.com/paper/2403.01988/) (2024)
- [Making DeepFakes more spurious: evading deep face forgery detection via trace removal attack](https://mlsanity.com/paper/2203.11433/) (2022)
- [An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape](https://mlsanity.com/paper/2404.16212/) (2024)
- [A Survey of Defenses Against AI-Generated Visual Media: Detection,Disruption, and Authentication](https://mlsanity.com/paper/2407.10575/) (2024)
- [AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection](https://mlsanity.com/paper/2312.08675/) (2023)
- [Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks](https://mlsanity.com/paper/2407.20836/) (2024)
- [Misleading Deep-Fake Detection with GAN Fingerprints](https://mlsanity.com/paper/2205.12543/) (2022)
- [Unsupervised Multimodal Deepfake Detection Using Intra- and Cross-Modal Inconsistencies](https://mlsanity.com/paper/2311.17088/) (2023)
- [On the Vulnerability of DeepFake Detectors to Attacks Generated by Denoising Diffusion Models](https://mlsanity.com/paper/2307.05397/) (2023)
- [Towards Robust GAN-generated Image Detection: a Multi-view Completion Representation](https://mlsanity.com/paper/2306.01364/) (2023)
- [RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection](https://mlsanity.com/paper/2307.10642/) (2023)
- [Deepfakes and Higher Education: A Research Agenda and Scoping Review of Synthetic Media](https://mlsanity.com/paper/2404.15601/) (2024)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection](https://mlsanity.com/paper/2403.16513/) (2024)
- [Black-Box Attack against GAN-Generated Image Detector with Contrastive Perturbation](https://mlsanity.com/paper/2211.03509/) (2022)
- [Towards Robust and Realible Multimodal Misinformation Recognition with Incomplete Modality](https://mlsanity.com/paper/2510.05839/) (2025)
- [FFR_FD: Effective and Fast Detection of DeepFakes Based on Feature Point Defects](https://mlsanity.com/paper/2107.02016/) (2021)
- [Exploring Depth Information for Detecting Manipulated Face Videos](https://mlsanity.com/paper/2411.18572/) (2024)
- [Realism to Deception: Investigating Deepfake Detectors Against Face Enhancement](https://mlsanity.com/paper/2509.07178/) (2025)
- [Zero-Shot Visual Deepfake Detection: Can AI Predict and Prevent Fake Content Before It's Created?](https://mlsanity.com/paper/2509.18461/) (2025)
- [Frequency Bias Matters: Diving into Robust and Generalized Deep Image Forgery Detection](https://mlsanity.com/paper/2511.19886/) (2025)
- [Phase4DFD: Multi-Domain Phase-Aware Attention for Deepfake Detection](https://mlsanity.com/paper/2601.05861/) (2026)
- [Deepfake detectors are DUMB: A benchmark to assess adversarial training robustness under transferability constraints](https://mlsanity.com/paper/2601.05986/) (2026)
- [Universal Anti-forensics Attack against Image Forgery Detection via Multi-modal Guidance](https://mlsanity.com/paper/2602.06530/) (2026)
- [Ghosts Beneath Textures: Texture-Relation Cues for Cross-Paradigm AI-Generated Image Detection](https://mlsanity.com/paper/2607.03862/) (2026)

## Abstract (arXiv)

At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image. Such artifact patterns can be easily exploited (by recent methods) for difference detection of real and GAN-synthesized images. However, the existing detection methods put much emphasis on the artifact patterns, which can become futile if such artifact patterns were reduced. Towards reducing the artifacts in the synthesized images, in this paper, we devise a simple yet powerful approach termed FakePolisher that performs shallow reconstruction of fake images through a learned linear dictionary, intending to effectively and efficiently reduce the artifacts introduced during image synthesis. The comprehensive evaluation on 3 state-of-the-art DeepFake detection methods and fake images generated by 16 popular GAN-based fake image generation techniques, demonstrates the effectiveness of our technique.Overall, through reducing artifact patterns, our technique significantly reduces the accuracy of the 3 state-of-the-art fake image detection methods, i.e., 47% on average and up to 93% in the worst case.