FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes

• URL: http://arxiv.org/abs/2204.01960v1
• Date: Tue, 5 Apr 2022 03:29:30 GMT
• Title: FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes
• Authors: Paarth Neekhara, Shehzeen Hussain, Xinqiao Zhang, Ke Huang, Julian McAuley, Farinaz Koushanfar
• Abstract summary: Deepfakes and manipulated media are becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. We introduce a deep learning based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels.
• Score: 25.277040616599336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deepfakes and manipulated media are becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. There have been several attempts at combating Deepfakes using machine learning classifiers. However, such classifiers do not generalize well to black-box image synthesis techniques and have been shown to be vulnerable to adversarial examples. To address these challenges, we introduce a deep learning based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. Instead of identifying and detecting fake media using visual artifacts, we propose to proactively embed a semi-fragile watermark into a real image so that we can prove its authenticity when needed. Our watermarking framework is designed to be fragile to facial manipulations or tampering while being robust to benign image-processing operations such as image compression, scaling, saturation, contrast adjustments etc. This allows images shared over the internet to retain the verifiable watermark as long as face-swapping or any other Deepfake modification technique is not applied. We demonstrate that FaceSigns can embed a 128 bit secret as an imperceptible image watermark that can be recovered with a high bit recovery accuracy at several compression levels, while being non-recoverable when unseen Deepfake manipulations are applied. For a set of unseen benign and Deepfake manipulations studied in our work, FaceSigns can reliably detect manipulated content with an AUC score of 0.996 which is significantly higher than prior image watermarking and steganography techniques.

Related papers

• Social Media Authentication and Combating Deepfakes using Semi-fragile Invisible Image Watermarking [6.246098300155482]
  We propose a semi-fragile image watermarking technique that embeds an invisible secret message into real images for media authentication. Our proposed framework is designed to be fragile to facial manipulations or tampering while being robust to benign image-processing operations and watermark removal attacks.
  arXiv  Detail & Related papers  (2024-10-02T18:05:03Z)
• RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees [33.61946642460661]
  This paper introduces a robust and agile watermark detection framework, dubbed as RAW. We employ a classifier that is jointly trained with the watermark to detect the presence of the watermark. We show that the framework provides provable guarantees regarding the false positive rate for misclassifying a watermarked image.
  arXiv  Detail & Related papers  (2024-01-23T22:00:49Z)
• Robustness of AI-Image Detectors: Fundamental Limits and Practical Attacks [47.04650443491879]
  We analyze the robustness of various AI-image detectors including watermarking and deepfake detectors. We show that watermarking methods are vulnerable to spoofing attacks where the attacker aims to have real images identified as watermarked ones.
  arXiv  Detail & Related papers  (2023-09-29T18:30:29Z)
• T2IW: Joint Text to Image & Watermark Generation [74.20148555503127]
  We introduce a novel task for the joint generation of text to image and watermark (T2IW) This T2IW scheme ensures minimal damage to image quality when generating a compound image by forcing the semantic feature and the watermark signal to be compatible in pixels. We demonstrate remarkable achievements in image quality, watermark invisibility, and watermark robustness, supported by our proposed set of evaluation metrics.
  arXiv  Detail & Related papers  (2023-09-07T16:12:06Z)
• Invisible Image Watermarks Are Provably Removable Using Generative AI [47.25747266531665]
  Invisible watermarks safeguard images' copyrights by embedding hidden messages only detectable by owners. We propose a family of regeneration attacks to remove these invisible watermarks. The proposed attack method first adds random noise to an image to destroy the watermark and then reconstructs the image.
  arXiv  Detail & Related papers  (2023-06-02T23:29:28Z)
• Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust [55.91987293510401]
  Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. We introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed.
  arXiv  Detail & Related papers  (2023-05-31T17:00:31Z)
• Certified Neural Network Watermarks with Randomized Smoothing [64.86178395240469]
  We propose a certifiable watermarking method for deep learning models. We show that our watermark is guaranteed to be unremovable unless the model parameters are changed by more than a certain l2 threshold. Our watermark is also empirically more robust compared to previous watermarking methods.
  arXiv  Detail & Related papers  (2022-07-16T16:06:59Z)
• CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes [74.18502861399591]
  Malicious application of deepfakes (i.e., technologies can generate target faces or face attributes) has posed a huge threat to our society. We propose a universal adversarial attack method on deepfake models, to generate a Cross-Model Universal Adversarial Watermark (CMUA-Watermark) Experimental results demonstrate that the proposed CMUA-Watermark can effectively distort the fake facial images generated by deepfake models.
  arXiv  Detail & Related papers  (2021-05-23T07:28:36Z)
• Watermark Faker: Towards Forgery of Digital Image Watermarking [10.14145437847397]
  We make the first attempt to develop digital image watermark fakers by using generative adversarial learning. Our experiments show that the proposed watermark faker can effectively crack digital image watermarkers in both spatial and frequency domains.
  arXiv  Detail & Related papers  (2021-03-23T12:28:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.