# Whether, Which, and Whose: Solving the Triple Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios

arXiv: https://arxiv.org/abs/2604.26342 · HTML: https://mlsanity.com/paper/2604.26342/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 40 references that are in the map.
- First posted on arXiv 29 Apr 2026.

## Builds on (most cited first, 30 of 31)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [WIDER FACE: A Face Detection Benchmark](https://mlsanity.com/paper/1511.06523/) (2015)
- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://mlsanity.com/paper/2106.06340/) (2021)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [Face Forensics in the Wild](https://mlsanity.com/paper/2103.16076/) (2021)
- [Finding Tiny Faces](https://mlsanity.com/paper/1612.04402/) (2016)
- [Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms](https://mlsanity.com/paper/2212.12678/) (2022)
- [Sharp Multiple Instance Learning for DeepFake Video Detection](https://mlsanity.com/paper/2008.04585/) (2020)
- [Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Models](https://mlsanity.com/paper/2412.03283/) (2024)
- [OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild](https://mlsanity.com/paper/2107.14480/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection](https://mlsanity.com/paper/2305.06321/) (2023)
- [IML-ViT: Benchmarking Image Manipulation Localization by Vision Transformer](https://mlsanity.com/paper/2307.14863/) (2023)
- [MobileFaceSwap: A Lightweight Framework for Video Face Swapping](https://mlsanity.com/paper/2201.03808/) (2022)
- [OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking](https://mlsanity.com/paper/2412.01615/) (2024)
- [Pixel-Inconsistency Modeling for Image Manipulation Localization](https://mlsanity.com/paper/2310.00234/) (2023)
- [LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks](https://mlsanity.com/paper/2411.17209/) (2024)
- [DiffMark: Diffusion-based Robust Watermark Against Deepfakes](https://mlsanity.com/paper/2507.01428/) (2025)
- [Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection](https://mlsanity.com/paper/2310.04845/) (2023)
- [WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks](https://mlsanity.com/paper/2505.08614/) (2025)
- [Seeing Through Deepfakes: A Human-Inspired Framework for Multi-Face Detection](https://mlsanity.com/paper/2507.14807/) (2025)
- [Uncovering and Mitigating Destructive Multi-Embedding Attacks in Deepfake Proactive Forensics](https://mlsanity.com/paper/2508.17247/) (2025)

## Abstract (arXiv)

Unlike single-face forgeries, deepfakes in complex multi-person interaction scenarios (such as group photos and multi-person meetings) more closely reflect real-world threats. Although existing proactive forensics solutions demonstrate good performance, they heavily rely on a "single-face" setting, making it difficult to effectively deal with the problems of deepfake detection, localization, and source tracing in complex multi-person environments. In this paper, we propose a Deep Attributable Watermarking Framework (DAWF) tailored to multi-face proactive forensics, which establishes an isolated identity attribution space. This spatial isolation ensures that multiple independent tracing signals can coexist within a single image and be successfully anchored to their respective identity instances. Crucially, we propose a selective regional supervision loss to suppress cross-face interference, guiding the decoder to focus exclusively on the manipulated facial regions. DAWF unifies image-level detection, instance-level localization, and identity-level source tracing, successfully achieving the "whether, which, and whose" forensic goals of determining whether an image is manipulated, which face is forged, and whose identity is tampered with. Extensive experiments on challenging multi-face datasets demonstrate robust triple-forensic performance, achieving an AUC of 0.91, an F1-score of 0.87, and a BER of 0.54%. The code is available at https://github.com/vpsg-research/DAWF.