Abstract: For real-world complex working conditions,deep learning-based multimodal(visible,infrared,etc.) target detectors improve the detection effect by fusing data features from different bands.However,it has been found that multimodal detectors are susceptible to adversarial attacks,resulting in the output detection frames being severely off-target or the detection frames disappearing,which reduces their reliability for use in the physical world.Work has been done to explore black-box physical adversarial attacks for multimodal depth detectors,but there are still problems such as inefficient modal attacks,limited target detectors and fusion strategies,and poor physical domain attack assignment.Aiming at the above problems,this paper proposes a multimodal adversarial color patch(MAC-Patch) generation method to achieve efficient,general,and robust attacks on multimodal deep detectors.Specifically,a stochastic gradient descent optimizer is utilized to generate strong adversarial patches against different modalities on the equivalent model,and can still effectively interfere with the target model in a black-box setting without accessing the internal structure of the target model.A patch location optimization method based on differential evolution is proposed to adaptively select the optimal attack location under multiple target fusion strategies,target detection models,and defense settings.Finally,the attack effectiveness,generalization and migration of MAC-Patch are tested on 2 models,3 image fusion strategies and 4 datasets respectively;the actual attack effect under different environment brightness and different patch rotation angles is adopted in the physical domain with expectation translation transformation to verify its robustness.Experimental results show that MAC-Patch is optimal in terms of attack success rate,AP reduction value and other indexes,such as compared with the three advanced attacks of MAP,MIC,and UAP,the AP reduction value of MAC-Patch is improved by 62.6%.

Key words: Object detection, Multimodality, Adversarial attack, Physical attack, Defense