摘要 Abstract 

模型逆向攻擊能從AI模型重建訓練資料，對隱私構成重大威脅。本次演講將介紹 Trap-MID，一種將陷阱觸發器（trapdoor trigger）嵌入模型的防禦方法，藉此誤導攻擊者重建出陷阱圖樣，而非真實隱私資料。與傳統正則化式防禦不同，Trap-MID 透過欺敵策略來保護隱私。我將展示此方法在各類模型逆向攻擊下達到最先進防禦效果，且無需額外資料或大量運算資源。

Model Inversion (MI) attacks threaten data privacy by reconstructing training data from AI models. This talk presents Trap-MID, a defense that embeds a trapdoor trigger into the model, causing MI attacks to recover the trapdoor pattern instead of private data. Unlike traditional regularization-based defenses, Trap-MID uses deception to mislead attackers. I will discuss theoretical insights on trapdoor effectiveness and naturalness, along with empirical results showing state-of-the-art performance against various MI attacks—achieved without extra data or significant overhead.