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Lecture Notes in Computer Science
In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasiondoi:10.1007/978-3-642-40994-3_25 fatcat:2pj7xgansrdazat7y74kafm2hi