Tootfinder

Neblogas penas pamąstymui.
https://jonasvalatkevicius.substack.com/p/nauseda-ruginiene-zemaitaitis-ir

FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Hossein Kashiani, Niloufar Alipour Talemi, Fatemeh Afghah
https://arxiv.org/abs/2509.22412

FreqDebias: Towards Generalizable Deepfake Detection via Consistency-Driven Frequency Debiasing
Deepfake detectors often struggle to generalize to novel forgery types due to biases learned from limited training data. In this paper, we identify a new type of model bias in the frequency domain, termed spectral bias, where detectors overly rely on specific frequency bands, restricting their ability to generalize across unseen forgeries. To address this, we propose FreqDebias, a frequency debiasing framework that mitigates spectral bias through two complementary strategies. First, we introduc…

Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation
Jaechul Roh, Zachary Novack, Yuefeng Peng, Niloofar Mireshghallah, Taylor Berg-Kirkpatrick, Amir Houmansadr
https://arxiv.org/abs/2507.17937

Bob's Confetti: Phonetic Memorization Attacks in Music and Video Generation
Lyrics-to-Song (LS2) generation models promise end-to-end music synthesis from text, yet their vulnerability to training data memorization remains underexplored. We introduce Adversarial PhoneTic Prompting (APT), a novel attack where lyrics are semantically altered while preserving their acoustic structure through homophonic substitutions (e.g., Eminem's famous "mom's spaghetti" $\rightarrow$ "Bob's confetti"). Despite these distortions, we uncover a powerful form of sub-lexical memorization: m…