4 papers
MIG-COW: Transferable Adversarial Attacks on Deepfake Detectors via Gradient Decomposition
Wonjune Seo, Joonhyuk Baek, Yeseong Jung +1
Despite recent advances, deepfake detectors remain vulnerable to adversarial examples, particularly in diverse, real-world settings. We propose MIG-COW, a novel adversarial attack…
Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data
Kiwan Kwon, Kangmin Kim, Hojin Lee +5
Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing and research, yet conventional tabular metrics often overlook temporal structure. Existi…
Unlearning's Blind Spots: Over-Unlearning and Prototypical Relearning Attack
SeungBum Ha, Saerom Park, Sung Whan Yoon
Machine unlearning (MU) aims to expunge a designated forget set from a trained model without costly retraining, yet the existing techniques overlook two critical blind spots: "over…
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability
Stephen Casper, Jieun Yun, Joonhyuk Baek +13
Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying…