2021/03/02 by Colin Paterson, Haoze Wu, Paterson, Colin +11
Computer Science · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.LG
paper · pdf · doi:10.48550/arxiv.2103.01629
arxiv created 2021/03/02 · openalex publication_date 2021/03/02 · arxiv updated 2021/03/03 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
We introduce DeepCert, a tool-supported method for verifying the robustness of deep neural network (DNN) image classifiers to contextually relevant perturbations such as blur, haze, and changes in image contrast. While the robustness of DNN classifiers has been the subject of intense research in recent years, the solutions delivered by this research focus on verifying DNN robustness to small perturbations in the images being classified, with perturbation magnitude measured using established Lp norms. This is useful for identifying potential adversarial attacks on DNN image classifiers, but cannot verify DNN robustness to contextually relevant image perturbations, which are typically not small when expressed with Lp norms. DeepCert addresses this underexplored verification problem by supporting:(1) the encoding of real-world image perturbations; (2) the systematic evaluation of contextually relevant DNN robustness, using both testing and formal verification; (3) the generation of contextually relevant counterexamples; and, through these, (4) the selection of DNN image classifiers suitable for the operational context (i)envisaged when a potentially safety-critical system is designed, or (ii)observed by a deployed system. We demonstrate the effectiveness of DeepCert by showing how it can be used to verify the robustness of DNN image classifiers build for two benchmark datasets (`German Traffic Sign' and `CIFAR-10') to multiple contextually relevant perturbations.