Generative Models are Self-Watermarked: Declaring Model Authentication through Re-Generation
As machine- and AI-generated content proliferates, protecting the intellectual property of generative models has become imperative, yet verifying data ownership poses formidable challenges, particularly in cases of unauthorized reuse of generated data. The challenge of verifying data ownership is further amplified by using Machine Learning as a Service (MLaaS), which often functions as a black-box system. Our work is dedicated to detecting data reuse from even an individual sample. Traditionally, watermarking has been leveraged to detect AI-generated content. However, unlike watermarking techniques that embed additional information as triggers into models or generated content, potentially compromising output quality, our approach identifies latent fingerprints inherently present within the outputs through re-generation. We propose an explainable verification procedure that attributes data ownership through re-generation, and further amplifies these fingerprints in the generative models through iterative data re-generation. This methodology is theoretically grounded and demonstrates viability and robustness using recent advanced text and image generative models. Our methodology is significant as it goes beyond protecting the intellectual property of APIs and addresses important issues such as the spread of misinformation and academic misconduct. It provides a useful tool to ensure the integrity of sources and authorship, expanding its application in different scenarios where authenticity and ownership verification are essential.
Code (0)
등록된 구현이 없습니다.
Tasks
MisinformationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
WSMN: An optimized multipurpose blind watermarking in Shearlet domain using MLP and NSGA-II
Digital watermarking is a remarkable issue in the field of information security to avoid the misuse of images in multimedia networks. Although access to unauthorized persons can be prevented through cryptography, it cann…
QuantizationSSIMReversible Quantization Index Modulation for Static Deep Neural Network Watermarking
Static deep neural network (DNN) watermarking techniques typically employ irreversible methods to embed watermarks into the DNN model weights. However, this approach causes permanent damage to the watermarked model and f…
QuantizationWatermarking Graph Neural Networks based on Backdoor Attacks
Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. Building a powerful GNN model is not a trivial task, as it requires a large amount of training data, powerful computing…
ClassificationGraph ClassificationModel extractionNode Classification+1Convolutional Neural Network-Based Image Watermarking using Discrete Wavelet Transform
With the growing popularity of the Internet, digital images are used and transferred more frequently. Although this phenomenon facilitates easy access to information, it also creates security concerns and violates intell…
Watermarking Training Data of Music Generation Models
Generative Artificial Intelligence (Gen-AI) models are increasingly used to produce content across domains, including text, images, and audio. While these models represent a major technical breakthrough, they gain their …
Music Generation