Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟮𝟲𝟱 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗪𝗮𝘁𝗰𝗵 𝗼𝘂𝘁! Motion is Blurring the Vision of Your Deep Neural Networks by Alibaba Group and Kyushu University, Japan Follow me for a similar post: 🇮🇳 Ashish Patel Interesting Facts : 🔸 This paper is published in NeuroIPS2020 with 19 citations. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 : https://lnkd.in/eAcWiG9B Code: https://lnkd.in/ec8365rF ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 The state-of-the-art deep neural networks (DNNs) are vulnerable against adversarial examples with additive random-like noise perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making the study of which greatly important especially for the widely adopted real-time image processing tasks (e.g., object detection, tracking).  🔸 In this paper, we initiate the first step to comprehensively investigate the potential hazards of the blur effect for DNN, caused by object motion. We propose a novel adversarial attack method that can generate visually natural motion-blurred adversarial examples, named motion-based adversarial blur attack (ABBA). To this end, we first formulate the kernel-prediction-based attack where an input image is convolved with kernels in a pixel-wise way, and the misclassification capability is achieved by tuning the kernel weights.  🔸 To generate visually more natural and plausible examples, we further propose the saliency-regularized adversarial kernel prediction, where the salient region serves as a moving object, and the predicted kernel is regularized to achieve natural visual effects. Besides, the attack is further enhanced by adaptively tuning the translations of objects and backgrounds.  🔸 A comprehensive evaluation of the NeurIPS'17 adversarial competition dataset demonstrates the effectiveness of ABBA by considering various kernel sizes, translations, and regions. The in-depth study further confirms that our method shows the more effective penetrating capability to the state-of-the-art GAN-based deblurring mechanisms compared with other blurring methods.  #computervision #artificialintelligence #machinelearning ------------------------------