This work introduces the first large-scale benchmark for this task by collecting real portraits and fake portraits from multiple sources and evaluates representative existing detectors on this benchmark, revealing their lack of explicit mechanisms for retaining fine-grained information and maintaining feature consisten...
Yu-Jie Gao, Zijian Yu, Yan Hong et al.· 0 citations
Advances in image generation have made synthetic images increasingly difficult to distinguish from real photographs, raising concerns about the trustworthiness of visual media. Existing AI-generated image detectors often perform well on in-domain data, but their robustness and cross-generator generalization remain limi...
Man-Ni Cui, Rui-Qi Liu, Zi-Jian Yu et al.· 0 citations
GAP-SAM encodes an image and its frozen VAE reconstruction into a global artifact token and injects it into SAM3's feature pyramid via zero-gated FiLM via zero-gated FiLM before pixel decoding to preserve localization while suppressing semantic-boundary shortcuts.
Haozhen Yan, Siyuan Shan, Zijian Yu et al.· 0 citations
This work proposes a causal framework based on activation steering to actively probe and manipulate internal visual representations, revealing a divergence in concept encoding: entity knowledge is distinctively localized, whereas abstract concepts are globally distributed across the network.
Experimental results demonstrate that S2V can consistently improve LVU performance across multiple LVU benchmarks, outperforming both general MLLMs and reasoning-based methods not only in LVU accuracy but also in training and inference efficiency.
Beibei Zhang, Chao Xu, Jun Lan et al.· 0 citations
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