9 accepted papers
Extracting reliable generative traces in generated images is critical for AI-generated images (AIGIs) detection. However, a fundamental challenge exists: AIGIs inherently contain generative traces with no trace-free counterpart available, making supervised extraction of these artifacts infeasible. I
Attributing synthetic images to their source generative models is critical for digital forensics and security. While most existing attribution methods can distinguish images produced by known models and reject those from unknown ones, they are unable to verify whether a given image was produced by a
Recently, adversarial attacks on speaker recognition systems have garnered significant interest. However, existing methods focus on injecting subtle perturbations into audio, which may compromise auditory quality. To address this problem, we propose a novel approach named DiffAttack, which employs a…
We introduce OmniMark, a novel and efficient fingerprinting method for Latent Diffusion Models (LDM). OmniMark can encode user-specific fingerprints across diverse dimensions of the weights of the LDM, including kernels, filters, channels, and spatial domains. The LDM is fine-tuned to encode the inv…
As AI generative models evolve, face swap technology has become increasingly accessible, raising concerns over potential misuse. Celebrities may be manipulated without consent, and ordinary individuals may fall victim to identity fraud. To address these threats, we propose Secure Swap, a method that…
The commercialization of generative artificial intelligence (GenAI) has led to a multi-level ecosystem involving model developers, service providers, and consumers. Thus, ensuring traceability is crucial, as service providers may violate intellectual property rights (IPR), and consumers may generate…
Latent Diffusion Models (LDMs) enable fine-tuning with only a few images and have become widely used on the Internet. However, it can also be misused to generate fake images, leading to privacy violations and social risks. Existing adversarial attack methods primarily introduce noise distortions to…
In this work we propose IDGuard a novel proactive defense method from the perspective of developers to protect Persons-of-Interest (POI) such as national leaders from face editing abuse. We build a bridge between identities and model behavior safeguarding POI identities rather than merely certain fa…
In this work, we propose a novel method to improve the generalization ability of CNN-based face forgery detectors. Our method considers the feature anomalies of forged faces caused by the prevalent blending operations in face forgery algorithms. Specifically, we propose a weakly supervised Second Or…