# Scapegoat Generation for Privacy Protection from Deepfake

arXiv: https://arxiv.org/abs/2303.02930 · HTML: https://mlsanity.com/paper/2303.02930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 18 references that are in the map.
- First posted on arXiv 6 Mar 2023.

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

- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Spatial-Phase Shallow Learning: Rethinking Face Forgery Detection in Frequency Domain](https://mlsanity.com/paper/2103.01856/) (2021)
- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://mlsanity.com/paper/2106.06340/) (2021)
- [HyperInverter: Improving StyleGAN Inversion via Hypernetwork](https://mlsanity.com/paper/2112.00719/) (2021)
- [Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems](https://mlsanity.com/paper/2003.01279/) (2020)
- [Adversarial Threats to DeepFake Detection: A Practical Perspective](https://mlsanity.com/paper/2011.09957/) (2020)
- [Defending against GAN-based Deepfake Attacks via Transformation-aware Adversarial Faces](https://mlsanity.com/paper/2006.07421/) (2020)
- [MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes](https://mlsanity.com/paper/2103.14211/) (2021)
- [Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction](https://mlsanity.com/paper/2102.00798/) (2021)

## Built on by (most cited first, 1 of 1)

- [Al'em do Desempenho: Um Estudo da Confiabilidade de Detectores de Deepfakes](https://mlsanity.com/paper/2601.08674/) (2026)

## Abstract (arXiv)

To protect privacy and prevent malicious use of deepfake, current studies propose methods that interfere with the generation process, such as detection and destruction approaches. However, these methods suffer from sub-optimal generalization performance to unseen models and add undesirable noise to the original image. To address these problems, we propose a new problem formulation for deepfake prevention: generating a ``scapegoat image'' by modifying the style of the original input in a way that is recognizable as an avatar by the user, but impossible to reconstruct the real face. Even in the case of malicious deepfake, the privacy of the users is still protected. To achieve this, we introduce an optimization-based editing method that utilizes GAN inversion to discourage deepfake models from generating similar scapegoats. We validate the effectiveness of our proposed method through quantitative and user studies.