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Seongho Kim

2 accepted papers

2026

Dissect and Prune: Enhancing Robustness in AI-Generated Image Detection

ICML 2026poster

While existing AI-generated image detectors report high performance, we identify that this is largely driven by a critical *prediction asymmetry*: a bias toward the real class that severely limits sensitivity to generated content, especially under standard post-processing operations such as compress…

Cited by 0SourceScholar
2024

All You Need is Your Voice: Emotional Face Representation with Audio Perspective for Emotional Talking Face Generation

ECCV 2024poster

"With the rise of generative models, multi-modal video generation has gained significant attention, particularly in the realm of audio-driven emotional talking face synthesis. This paper addresses two key challenges in this domain: Input bias and intensity saturation. A novel neutralization scheme i…