507 research outputs found
Models and Algorithms for Graph Watermarking
We introduce models and algorithmic foundations for graph watermarking. Our
frameworks include security definitions and proofs, as well as
characterizations when graph watermarking is algorithmically feasible, in spite
of the fact that the general problem is NP-complete by simple reductions from
the subgraph isomorphism or graph edit distance problems. In the digital
watermarking of many types of files, an implicit step in the recovery of a
watermark is the mapping of individual pieces of data, such as image pixels or
movie frames, from one object to another. In graphs, this step corresponds to
approximately matching vertices of one graph to another based on graph
invariants such as vertex degree. Our approach is based on characterizing the
feasibility of graph watermarking in terms of keygen, marking, and
identification functions defined over graph families with known distributions.
We demonstrate the strength of this approach with exemplary watermarking
schemes for two random graph models, the classic Erd\H{o}s-R\'{e}nyi model and
a random power-law graph model, both of which are used to model real-world
networks
Spread spectrum-based video watermarking algorithms for copyright protection
Merged with duplicate record 10026.1/2263 on 14.03.2017 by CS (TIS)Digital technologies know an unprecedented expansion in the last years. The consumer can
now benefit from hardware and software which was considered state-of-the-art several years
ago. The advantages offered by the digital technologies are major but the same digital
technology opens the door for unlimited piracy. Copying an analogue VCR tape was certainly
possible and relatively easy, in spite of various forms of protection, but due to the analogue
environment, the subsequent copies had an inherent loss in quality. This was a natural way of
limiting the multiple copying of a video material. With digital technology, this barrier
disappears, being possible to make as many copies as desired, without any loss in quality
whatsoever. Digital watermarking is one of the best available tools for fighting this threat.
The aim of the present work was to develop a digital watermarking system compliant with the
recommendations drawn by the EBU, for video broadcast monitoring. Since the watermark
can be inserted in either spatial domain or transform domain, this aspect was investigated and
led to the conclusion that wavelet transform is one of the best solutions available. Since
watermarking is not an easy task, especially considering the robustness under various attacks
several techniques were employed in order to increase the capacity/robustness of the system:
spread-spectrum and modulation techniques to cast the watermark, powerful error correction
to protect the mark, human visual models to insert a robust mark and to ensure its invisibility.
The combination of these methods led to a major improvement, but yet the system wasn't
robust to several important geometrical attacks. In order to achieve this last milestone, the
system uses two distinct watermarks: a spatial domain reference watermark and the main
watermark embedded in the wavelet domain. By using this reference watermark and techniques
specific to image registration, the system is able to determine the parameters of the attack and
revert it. Once the attack was reverted, the main watermark is recovered. The final result is a
high capacity, blind DWr-based video watermarking system, robust to a wide range of attacks.BBC Research & Developmen
A collusion attack on digital video watermarks based on the replacement strategy
Digital works such as images, audio and video present security concerns due to their portability and error free reproducibility. Thus, digital work producers are not being properly compensated for copyrighted works that are illegally copied and distributed on the Internet. One solution that has been proposed to solve some of these problems is digital watermarking. Researchers have proposed many different watermarking methods, but for any of these methods to be commercially applicable, they must be secure in the sense of being resilient to all known watermarking attacks. Therefore, the exploration and examination of watermarking attacks must be exhaustive. This paper adds to the knowledge base of known watermarking attacks on digital video. Specifically a type of collusion attack based on the replacement attack strategy is applied and tested against two digital video watermarking schemes. The effectiveness of this attack is measured by evaluating the fidelity of the attacked video as well as the ability of the attack to remove the watermark. This attack will provide yet another quality standard for measuring the effectiveness of watermarking schemes. This standard must be met if watermarking is to be a commercially viable option
Robust Object-Based Watermarking Using SURF Feature Matching and DFT Domain
In this paper we propose a robust object-based watermarking method, in which the watermark is embedded into the middle frequencies band of the Discrete Fourier Transform (DFT) magnitude of the selected object region, altogether with the Speeded Up Robust Feature (SURF) algorithm to allow the correct watermark detection, even if the watermarked image has been distorted. To recognize the selected object region after geometric distortions, during the embedding process the SURF features are estimated and stored in advance to be used during the detection process. In the detection stage, the SURF features of the distorted image are estimated and match them with the stored ones. From the matching result, SURF features are used to compute the Affine-transformation parameters and the object region is recovered. The quality of the watermarked image is measured using the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and the Visual Information Fidelity (VIF). The experimental results show the proposed method provides robustness against several geometric distortions, signal processing operations and combined distortions. The receiver operating characteristics (ROC) curves also show the desirable detection performance of the proposed method. The comparison with a previously reported methods based on different techniques is also provided
REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language Models
We present REMARK-LLM, a novel efficient, and robust watermarking framework
designed for texts generated by large language models (LLMs). Synthesizing
human-like content using LLMs necessitates vast computational resources and
extensive datasets, encapsulating critical intellectual property (IP). However,
the generated content is prone to malicious exploitation, including spamming
and plagiarism. To address the challenges, REMARK-LLM proposes three new
components: (i) a learning-based message encoding module to infuse binary
signatures into LLM-generated texts; (ii) a reparameterization module to
transform the dense distributions from the message encoding to the sparse
distribution of the watermarked textual tokens; (iii) a decoding module
dedicated for signature extraction; Furthermore, we introduce an optimized beam
search algorithm to guarantee the coherence and consistency of the generated
content. REMARK-LLM is rigorously trained to encourage the preservation of
semantic integrity in watermarked content, while ensuring effective watermark
retrieval. Extensive evaluations on multiple unseen datasets highlight
REMARK-LLM proficiency and transferability in inserting 2 times more signature
bits into the same texts when compared to prior art, all while maintaining
semantic integrity. Furthermore, REMARK-LLM exhibits better resilience against
a spectrum of watermark detection and removal attacks
Provable Robust Watermarking for AI-Generated Text
We study the problem of watermarking large language models (LLMs) generated
text -- one of the most promising approaches for addressing the safety
challenges of LLM usage. In this paper, we propose a rigorous theoretical
framework to quantify the effectiveness and robustness of LLM watermarks. We
propose a robust and high-quality watermark method, Unigram-Watermark, by
extending an existing approach with a simplified fixed grouping strategy. We
prove that our watermark method enjoys guaranteed generation quality,
correctness in watermark detection, and is robust against text editing and
paraphrasing. Experiments on three varying LLMs and two datasets verify that
our Unigram-Watermark achieves superior detection accuracy and comparable
generation quality in perplexity, thus promoting the responsible use of LLMs.
Code is available at https://github.com/XuandongZhao/Unigram-Watermark
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