Diffusion-based Image Generation of Oracle Bone Inscription Style Characters

Supervisor: 謝 浩然先端科学技術研究科修士 (情報科学

FPGA向けのオフチップメモリ参照を削減したブロックベースCNNアクセラレータに関する研究

Supervisor: 田中 清史先端科学技術研究科博

価値要素を用いたIoTビジネスモデルの価値提案発想法

Supervisor: 内平 直志先端科学技術研究科博

Formal and Experimental Verification of Robot Control Protocols for Smart Buildings

Supervisor: BEURAN, Razvan Florin先端科学技術研究科修士 (情報科学

メトホルミン内包緑茶カテキン・ナノ粒子の合成と特性評価

Supervisor: 栗澤 元一先端科学技術研究科修士 (マテリアルサイセンス

VRを用いた筋力トレーニングの基本動作の習得支援

Supervisor: 藤波 努先端科学技術研究科修士 (知識科学

万葉集の未解読歌の解読

Supervisor: 白井 清昭先端科学技術研究科修士 (情報科学

分散可能なコンテナ間動的スケジューリング最適化

Supervisor: 宇多 仁先端科学技術研究科修士 (情報科学

メタンからのメタノール合成を目的としたハイスループットスクリーニング

Supervisor: 谷池 俊明先端科学技術研究科修士 (マテリアルサイセンス

Shortcut-enhanced Multimodal Backdoor Attack in Vision-guided Robot Grasping

Integrating the Artificial Intelligence (AI) vision module into the robot grasping system can significantly improve its generalizability, thereby enhancing the efficiency of Human-Robot Interaction (HRI). However, the inherent lack of interpretability in AI also opens the gate to external threats. In this work, we reveal a novel safety risk in this vision-guided robot grasping system by proposing the Shortcut-enhanced Multimodal Backdoor Attack (SEMBA), which can manipulate the grasp quality score using the backdoor trigger leading to a misguided grasping sequence. The SEMBA may thus cause potentially hazardous grasping and pose a threat to human safety in HRI. Specifically, we initially present the Multimodal Shortcut Searching Algorithm (MSSA) to find the pixel value that deviates the most from the mean and standard deviation of the multimodal dataset, along with the pivotal pixel position for individual images. This will guarantee that the proposed attack is effective in complex, multi-class object scenarios. Next, based on MSSA, we devise the Multimodal Trigger Generator (MTG) to create diverse multimodal backdoor triggers and integrate them into the dataset, ensuring that our attack has the multimodality attribute. We conduct extensive experiments on the benchmark datasets and a cobot, showing the effectiveness of the proposed method both in the digital and physical worlds. Our demo videos are available in supplementary items