# All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark

arXiv: https://arxiv.org/abs/2602.23523 · HTML: https://mlsanity.com/paper/2602.23523/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Watermarking generated content and models, one of 12 directions in LLM safety and security.
- Builds on 30 of its 48 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Placed in

- LLM safety and security / [Watermarking generated content and models](https://mlsanity.com/safety/watermarking-generated-content-and-models/)

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

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)](https://mlsanity.com/paper/1703.07332/) (2017)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Embedding Watermarks into Deep Neural Networks](https://mlsanity.com/paper/1701.04082/) (2017)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://mlsanity.com/paper/2106.06340/) (2021)
- [DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection](https://mlsanity.com/paper/2001.00179/) (2020)
- [EditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection](https://mlsanity.com/paper/2312.08883/) (2023)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems](https://mlsanity.com/paper/2003.01279/) (2020)
- [Towards Blind Watermarking: Combining Invertible and Non-invertible Mechanisms](https://mlsanity.com/paper/2212.12678/) (2022)
- [Deepfake Detection by Human Crowds, Machines, and Machine-informed Crowds](https://mlsanity.com/paper/2105.06496/) (2021)
- [Deepfake Detection: A Comprehensive Survey from the Reliability Perspective](https://mlsanity.com/paper/2211.10881/) (2022)
- [Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection](https://mlsanity.com/paper/2403.01786/) (2024)
- [FakeTagger: Robust Safeguards against DeepFake Dissemination via Provenance Tracking](https://mlsanity.com/paper/2009.09869/) (2020)
- [SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection](https://mlsanity.com/paper/2305.06321/) (2023)
- [Anti-Forgery: Towards a Stealthy and Robust DeepFake Disruption Attack via Adversarial Perceptual-aware Perturbations](https://mlsanity.com/paper/2206.00477/) (2022)
- [Proactive Image Manipulation Detection](https://mlsanity.com/paper/2203.15880/) (2022)
- [InvisMark: Invisible and Robust Watermarking for AI-generated Image Provenance](https://mlsanity.com/paper/2411.07795/) (2024)
- [LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks](https://mlsanity.com/paper/2411.17209/) (2024)
- [Exposing Deepfake Face Forgeries with Guided Residuals](https://mlsanity.com/paper/2205.00753/) (2022)
- [Robust Identity Perceptual Watermark Against Deepfake Face Swapping](https://mlsanity.com/paper/2311.01357/) (2023)
- [FaceGuard: Proactive Deepfake Detection](https://mlsanity.com/paper/2109.05673/) (2021)
- [DiffMark: Diffusion-based Robust Watermark Against Deepfakes](https://mlsanity.com/paper/2507.01428/) (2025)
- [WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks](https://mlsanity.com/paper/2505.08614/) (2025)
- [Face Deepfakes -- A Comprehensive Review](https://mlsanity.com/paper/2502.09812/) (2025)
- [Uncovering and Mitigating Destructive Multi-Embedding Attacks in Deepfake Proactive Forensics](https://mlsanity.com/paper/2508.17247/) (2025)

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

- [Whether, Which, and Whose: Solving the Triple Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios](https://mlsanity.com/paper/2604.26342/) (2026)
- [GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking](https://mlsanity.com/paper/2604.26519/) (2026)

## Abstract (arXiv)

With the rapid advancement of deepfake technology, malicious face manipulations pose a significant threat to personal privacy and social security. However, existing proactive forensics methods typically treat deepfake detection, tampering localization, and source tracing as independent tasks, lacking a unified framework to address them jointly. To bridge this gap, we propose a unified proactive forensics framework that jointly addresses these three core tasks. Our core framework adopts an innovative 152-dimensional landmark-identity watermark termed LIDMark, which structurally interweaves facial landmarks with a unique source identifier. To robustly extract the LIDMark, we design a novel Factorized-Head Decoder (FHD). Its architecture factorizes the shared backbone features into two specialized heads (i.e., regression and classification), robustly reconstructing the embedded landmarks and identifier, respectively, even when subjected to severe distortion or tampering. This design realizes an "all-in-one" trifunctional forensic solution: the regression head underlies an "intrinsic-extrinsic" consistency check for detection and localization, while the classification head robustly decodes the source identifier for tracing. Extensive experiments show that the proposed LIDMark framework provides a unified, robust, and imperceptible solution for the detection, localization, and tracing of deepfake content. The code is available at https://github.com/vpsg-research/LIDMark.