3 citations · 3 across the 5 of their papers we have counts for
7 papers
FARE: Forensic Acceptance Region Estimation for Catching Bait-and-Switch Image Generators
Kai Yao, Marc Juarez
Modern AI image generators are increasingly deployed as opaque APIs, where customers can query the deployed service, but cannot inspect model weights or architecture. This creates…
What Breaks Local Watermarks? A Robustness Benchmark for Local Invisible Image Watermarking
Kai Yao, Bence Szilágyi, Sebestyén Kamp +4
Local image watermarking embeds an invisible signal into selected image regions rather than spreading it across the entire image, enabling payload recovery from specific objects or…
Smudged Fingerprints: A Systematic Evaluation of the Robustness of AI Image Fingerprints
Kai Yao, Marc Juarez
Model fingerprint detection has shown promise to trace the provenance of AI-generated images in forensic applications. However, despite the inherent adversarial nature of these app…
Toward Quantitative Modeling of Cybersecurity Risks Due to AI Misuse
Steve Barrett, Malcolm Murray, Otter Quarks +17
Advanced AI systems offer substantial benefits but also introduce risks. In 2025, AI-enabled cyber offense has emerged as a concrete example. This technical report applies a quanti…
SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction
Kai Yao, Marc Juarez
Detecting the source model of AI-generated images is a growing accountability problem. AI fingerprinting techniques address this by detecting imperceptible patterns in the images t…
A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks
Junhua Lin, Marc Juarez
We present a novel attack specifically designed against Tree-Ring, a watermarking technique for diffusion models known for its high imperceptibility and robustness against removal…
SoK: What Makes Private Learning Unfair?
Kai Yao, Marc Juarez
Differential privacy has emerged as the most studied framework for privacy-preserving machine learning. However, recent studies show that enforcing differential privacy guarantees…