Physical world assistive signals for deep neural network classifiers -- neither defense nor attack

• URL: http://arxiv.org/abs/2105.00622v1
• Date: Mon, 3 May 2021 04:02:48 GMT
• Title: Physical world assistive signals for deep neural network classifiers -- neither defense nor attack
• Authors: Camilo Pestana, Wei Liu, David Glance, Robyn Owens, Ajmal Mian
• Abstract summary: We introduce the concept of Assistive Signals, which are optimized to improve a model's confidence score regardless if it's under attack or not. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models. We discuss how we can exploit these insights to re-think, or avoid, some patterns that might contribute to, or degrade, the detectability of objects in the real-world.
• Score: 23.138996515998347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep Neural Networks lead the state of the art of computer vision tasks. Despite this, Neural Networks are brittle in that small changes in the input can drastically affect their prediction outcome and confidence. Consequently and naturally, research in this area mainly focus on adversarial attacks and defenses. In this paper, we take an alternative stance and introduce the concept of Assistive Signals, which are optimized to improve a model's confidence score regardless if it's under attack or not. We analyse some interesting properties of these assistive perturbations and extend the idea to optimize assistive signals in the 3D space for real-life scenarios simulating different lighting conditions and viewing angles. Experimental evaluations show that the assistive signals generated by our optimization method increase the accuracy and confidence of deep models more than those generated by conventional methods that work in the 2D space. In addition, our Assistive Signals illustrate the intrinsic bias of ML models towards certain patterns in real-life objects. We discuss how we can exploit these insights to re-think, or avoid, some patterns that might contribute to, or degrade, the detectability of objects in the real-world.

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