ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network

• URL: http://arxiv.org/abs/2305.06786v1
• Date: Thu, 11 May 2023 13:21:29 GMT
• Title: ReMark: Receptive Field based Spatial WaterMark Embedding Optimization using Deep Network
• Authors: Natan Semyonov, Rami Puzis, Asaf Shabtai, Gilad Katz
• Abstract summary: We investigate a novel deep learning-based architecture for embedding imperceptible watermarks. The proposed method is robust against most common distortions on watermarks including collusive distortion.
• Score: 23.357707056321534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Watermarking is one of the most important copyright protection tools for digital media. The most challenging type of watermarking is the imperceptible one, which embeds identifying information in the data while retaining the latter's original quality. To fulfill its purpose, watermarks need to withstand various distortions whose goal is to damage their integrity. In this study, we investigate a novel deep learning-based architecture for embedding imperceptible watermarks. The key insight guiding our architecture design is the need to correlate the dimensions of our watermarks with the sizes of receptive fields (RF) of modules of our architecture. This adaptation makes our watermarks more robust, while also enabling us to generate them in a way that better maintains image quality. Extensive evaluations on a wide variety of distortions show that the proposed method is robust against most common distortions on watermarks including collusive distortion.

Related papers

• Certifiably Robust Image Watermark [57.546016845801134]
  Generative AI raises many societal concerns such as boosting disinformation and propaganda campaigns. Watermarking AI-generated content is a key technology to address these concerns. We propose the first image watermarks with certified robustness guarantees against removal and forgery attacks.
  arXiv  Detail & Related papers  (2024-07-04T17:56:04Z)
• Evaluating Durability: Benchmark Insights into Multimodal Watermarking [36.12198778931536]
  We study robustness of watermarked content generated by image and text generation models against common real-world image corruptions and text perturbations. Our results could pave the way for the development of more robust watermarking techniques in the future.
  arXiv  Detail & Related papers  (2024-06-06T03:57:08Z)
• WAVES: Benchmarking the Robustness of Image Watermarks [67.955140223443]
  WAVES (Watermark Analysis Via Enhanced Stress-testing) is a benchmark for assessing image watermark robustness. We integrate detection and identification tasks and establish a standardized evaluation protocol comprised of a diverse range of stress tests. We envision WAVES as a toolkit for the future development of robust watermarks.
  arXiv  Detail & Related papers  (2024-01-16T18:58:36Z)
• TrustMark: Universal Watermarking for Arbitrary Resolution Images [21.74309490023683]
  Imperceptible digital watermarking is important in copyright protection, misinformation prevention and responsible generative GAN. We propose a GAN-based watermarking method with novel design in architecture and introduce TrustMark-RM - a watermark remover method. Our methods achieve state-of-art performance on 3 benchmarks comprising arbitrary encoded images.
  arXiv  Detail & Related papers  (2023-11-30T07:03:36Z)
• 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)
• Adaptive Blind Watermarking Using Psychovisual Image Features [8.75217589103206]
  This paper proposes an adaptive method that determines the strength of the watermark embedding in different parts of the cover image. Experimental results also show that the proposed method can effectively reconstruct the embedded payload in different kinds of common watermarking attacks.
  arXiv  Detail & Related papers  (2022-12-25T06:33:36Z)
• Watermarking Images in Self-Supervised Latent Spaces [75.99287942537138]
  We revisit watermarking techniques based on pre-trained deep networks, in the light of self-supervised approaches. We present a way to embed both marks and binary messages into their latent spaces, leveraging data augmentation at marking time.
  arXiv  Detail & Related papers  (2021-12-17T15:52:46Z)
• Exploring Structure Consistency for Deep Model Watermarking [122.38456787761497]
  The intellectual property (IP) of Deep neural networks (DNNs) can be easily stolen'' by surrogate model attack. We propose a new watermarking methodology, namely structure consistency'', based on which a new deep structure-aligned model watermarking algorithm is designed.
  arXiv  Detail & Related papers  (2021-08-05T04:27:15Z)
• Robust Watermarking using Diffusion of Logo into Autoencoder Feature Maps [10.072876983072113]
  In this paper, we propose to use an end-to-end network for watermarking. We use a convolutional neural network (CNN) to control the embedding strength based on the image content. Different image processing attacks are simulated as a network layer to improve the robustness of the model.
  arXiv  Detail & Related papers  (2021-05-24T05:18:33Z)
• Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models [72.9364216776529]
  We propose a novel watermark removal attack from a different perspective. We design a simple yet powerful transformation algorithm by combining imperceptible pattern embedding and spatial-level transformations. Our attack can bypass state-of-the-art watermarking solutions with very high success rates.
  arXiv  Detail & Related papers  (2020-09-18T09:14:54Z)

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.