As generated images become increasingly realistic, reliable forgery detection is essential for maintaining trust in visual information. However, existing methods primarily rely on task-specific supervision to adapt vision foundation model representations, without fully exploiting internal forensic knowledge to guide de...
Jia-Hua Li, Zixu John, Tom Zhong et al.· 0 citations
Multimodal representation learning seeks shared representations for cross-modal retrieval and knowledge transfer. Hub-based binding reduces pairwise supervision costs, but separate hub connections cannot guarantee reliable alignment between modalities without direct joint training. We introduce Hub-Spectral Activation...
Yingying Guo, Hai-Dong Chen, Lin-Rui Xu et al.· 0 citations
Emotional expression can influence the safety decisions of large language models (LLMs), offering a potential avenue for improving safety alignment. Existing studies have mainly focused on how emotional expressions facilitate attacks under harmful requests, while overlooking their effects on benign requests. We find th...
Shu-Yi Miao, Yao-Jin Ma, Chen-Hang Cui et al.· 0 citations
Uncovering the internal mechanisms underlying the safety capabilities of large language models (LLMs) is crucial for developing trustworthy artificial intelligence. Currently, mechanistic interpretability studies on multilingual safety are largely confined to local components, such as isolated neurons. However, this st...
Shu-Yi Miao, Wang-Jie Qiu, Pengyang Shao et al.· 0 citations
This work proposes aligning cross-lingual thoughts and responses (ACTR), a framework that improves multilingual safety alignment by strengthening the use of existing safety reasoning, and devise neuron-selective consistency optimization (NSCO), which uses a frozen judge model to reward agreement between the safety cate...
Xian-Hui Zhang, Jian Yu, Cheng-Yu Xie et al.· 0 citations
SafeNexus is introduced, a cross-modal safety alignment framework that adopts a dedicated neuron-level intervention strategy that outperforms prevailing state-of-the-art approaches on safety benchmarks spanning diverse modality combinations, while effectively preserving utility.
Jian Yu, Fei Shen, Cong Wang et al.· arXiv.org· 0 citations
With the rapid release of open-weight large foundation models, safety threats are shifting from black-box jailbreaks to neuron-level white-box attacks that directly identify and manipulate safety-related neurons. Existing alignment methods often investigate the safety behavior on a small number of neurons, creating fra...
Simiao Xie, Chuan-Cheng Shi, Shang-Ze Li et al.· 0 citations
LaP-Forensics is presented, a multimodal framework that augments RGB semantics with reconstruction-based forensic evidence that supports the utility of the residual stream under the evaluated settings, while free-form textual faithfulness and reliability under post-processing remain open limitations.
Can Wang, Yuhao Wang, Yu-She Cao et al.· arXiv.org· 1 citation
As generated videos become increasingly realistic, reliable video forgery detection is increasingly important. Existing studies typically optimize and use video forgery detectors as black boxes, while the latent forgery-discriminative knowledge inside them remains largely unexplored. Instead of continuing to rely on re...
Shi-Chao Kan, Chengpeng Hong, Jingtong Dou et al.· 0 citations
A neuron-level cross-dimensional safety alignment framework driven by modality- and language-shared safety neurons (MLS-Neurons) that significantly outperforms state-of-the-art approaches across diverse multilingual and multimodal safety benchmarks while preserving general utility.
Enyi Shi, Fei Shen, Chuancheng Shi et al.· arXiv.org· 0 citations
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