# LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks

arXiv: https://arxiv.org/abs/2411.17209 · HTML: https://mlsanity.com/paper/2411.17209/

## Key facts

- Cited by 21 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 37 of its 56 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## 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 37)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [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)
- [MesoNet: a Compact Facial Video Forgery Detection Network](https://mlsanity.com/paper/1809.00888/) (2018)
- [HiDDeN: Hiding Data With Deep Networks](https://mlsanity.com/paper/1807.09937/) (2018)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [Detecting Deepfakes with Self-Blended Images](https://mlsanity.com/paper/2204.08376/) (2022)
- [FSGAN: Subject Agnostic Face Swapping and Reenactment](https://mlsanity.com/paper/1908.05932/) (2019)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Generalizing Face Forgery Detection with High-frequency Features](https://mlsanity.com/paper/2103.12376/) (2021)
- [SimSwap: An Efficient Framework For High Fidelity Face Swapping](https://mlsanity.com/paper/2106.06340/) (2021)
- [Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data](https://mlsanity.com/paper/2007.08457/) (2020)
- [Learning Self-Consistency for Deepfake Detection](https://mlsanity.com/paper/2012.09311/) (2020)
- [DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection](https://mlsanity.com/paper/2307.01426/) (2023)
- [DeepFaceLab: Integrated, flexible and extensible face-swapping framework](https://mlsanity.com/paper/2005.05535/) (2020)
- [Two-Stream Neural Networks for Tampered Face Detection](https://mlsanity.com/paper/1803.11276/) (2018)
- [Implicit Identity Leakage: The Stumbling Block to Improving Deepfake Detection Generalization](https://mlsanity.com/paper/2210.14457/) (2022)
- [Fast Face-swap Using Convolutional Neural Networks](https://mlsanity.com/paper/1611.09577/) (2016)
- [MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression](https://mlsanity.com/paper/2108.08211/) (2021)
- [Dual Contrastive Learning for General Face Forgery Detection](https://mlsanity.com/paper/2112.13522/) (2021)
- [ReenactGAN: Learning to Reenact Faces via Boundary Transfer](https://mlsanity.com/paper/1807.11079/) (2018)
- [Fine-Grained Face Swapping via Regional GAN Inversion](https://mlsanity.com/paper/2211.14068/) (2022)
- [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 Based on the Discrepancy Between the Face and its Context](https://mlsanity.com/paper/2008.12262/) (2020)
- [CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes](https://mlsanity.com/paper/2105.10872/) (2021)
- [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)
- [Initiative Defense against Facial Manipulation](https://mlsanity.com/paper/2112.10098/) (2021)

## Built on by (most cited first, 21 of 21)

- [NullSwap: Proactive Identity Cloaking Against Deepfake Face Swapping](https://mlsanity.com/paper/2503.18678/) (2025)
- [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)
- [FractalForensics: Proactive Deepfake Detection and Localization via Fractal Watermarks](https://mlsanity.com/paper/2504.09451/) (2025)
- [Towards Generalized Proactive Defense against Face Swapping with Contour-Hybrid Watermark](https://mlsanity.com/paper/2505.19081/) (2025)
- [Continual Optimization with Symmetry Teleportation for Multi-Task Learning](https://mlsanity.com/paper/2503.04046/) (2025)
- [Uncovering and Mitigating Destructive Multi-Embedding Attacks in Deepfake Proactive Forensics](https://mlsanity.com/paper/2508.17247/) (2025)
- [All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark-Identity Watermark](https://mlsanity.com/paper/2602.23523/) (2026)
- [PVLM: Parsing-Aware Vision Language Model with Dynamic Contrastive Learning for Zero-Shot Deepfake Attribution](https://mlsanity.com/paper/2504.14129/) (2025)
- [Deep Data Hiding for ICAO-Compliant Face Images: A Survey](https://mlsanity.com/paper/2508.19324/) (2025)
- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)
- [Defending Deepfake via Texture Feature Perturbation](https://mlsanity.com/paper/2508.17315/) (2025)
- [Disruptive Attacks on Face Swapping via Low-Frequency Perceptual Perturbations](https://mlsanity.com/paper/2508.20595/) (2025)
- [DeepForgeSeal: Latent Space-Driven Semi-Fragile Watermarking for Deepfake Detection Using Adversarial Reinforcement Learning](https://mlsanity.com/paper/2511.04949/) (2025)
- [Transferable Multi-Bit Watermarking Across Frozen Diffusion Models via Latent Consistency Bridges](https://mlsanity.com/paper/2603.20304/) (2026)
- [SAiW: Source-Attributable Invisible Watermarking for Proactive Deepfake Defense](https://mlsanity.com/paper/2603.23178/) (2026)
- [High-Fidelity Face Content Recovery via Tamper-Resilient Versatile Watermarking](https://mlsanity.com/paper/2603.23940/) (2026)
- [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)
- [Enhancing Self-Supervised Talking Head Forgery Detection via a Training-Free Dual-System Framework](https://mlsanity.com/paper/2605.03390/) (2026)
- [Can Tainted Pixels Expose Deepfake Videos?](https://mlsanity.com/paper/2608.27492/) (2026)

## Abstract (arXiv)

Deepfake facial manipulation has garnered significant public attention due to its impacts on enhancing human experiences and posing privacy threats. Despite numerous passive algorithms that have been attempted to thwart malicious Deepfake attacks, they mostly struggle with the generalizability challenge when confronted with hyper-realistic synthetic facial images. To tackle the problem, this paper proposes a proactive Deepfake detection approach by introducing a novel training-free landmark perceptual watermark, LampMark for short. We first analyze the structure-sensitive characteristics of Deepfake manipulations and devise a secure and confidential transformation pipeline from the structural representations, i.e. facial landmarks, to binary landmark perceptual watermarks. Subsequently, we present an end-to-end watermarking framework that imperceptibly and robustly embeds and extracts watermarks concerning the images to be protected. Relying on promising watermark recovery accuracies, Deepfake detection is accomplished by assessing the consistency between the content-matched landmark perceptual watermark and the robustly recovered watermark of the suspect image. Experimental results demonstrate the superior performance of our approach in watermark recovery and Deepfake detection compared to state-of-the-art methods across in-dataset, cross-dataset, and cross-manipulation scenarios.