{"ID":2864343,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.23895","arxiv_id":"2509.23895","title":"Preserving Cross-Modal Stability for Visual Unlearning in Multimodal Scenarios","abstract":"Visual modality is the most vulnerable to privacy leakage in real-world multimodal applications like autonomous driving with visual and radar data; Machine unlearning removes specific training data from pre-trained models to address privacy leakage, however, existing methods fail to preserve cross-modal knowledge and maintain intra-class structural stability of retain data, leading to reduced overall and other modalities' performance during visual unlearning; to address these challenges, we propose a Cross-modal Contrastive Unlearning (CCU) framework, which integrates three key components: (a) selective visual unlearning: employing inverse contrastive learning to dissociate visual representations from their original semantics, (b) cross-modal knowledge retention: preserving other modalities' discriminability through semantic consistency, and (c) dual-set contrastive separation: preserving the model performance via isolation of structural perturbations between the unlearn set and retain set; extensive experiments on three datasets demonstrate the superiority of CCU, and our method achieves a 7.12% accuracy improvement with only 7% of the unlearning time compared to the top-accuracy baseline.","short_abstract":"Visual modality is the most vulnerable to privacy leakage in real-world multimodal applications like autonomous driving with visual and radar data; Machine unlearning removes specific training data from pre-trained models to address privacy leakage, however, existing methods fail to preserve cross-modal knowledge and m...","url_abs":"https://arxiv.org/abs/2509.23895","url_pdf":"https://arxiv.org/pdf/2509.23895v1","authors":"[\"Jinghan Xu Yuyang Zhang Qixuan Cai Jiancheng Chen Keqiu Li\"]","published":"2025-09-28T14:03:37Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false}