Flipping Sign Bits is All You Need to Craft Black-Box Adversarial Examples

Jun 14, 2019



As machine learning has increasingly been deployed in critical real-world applications, the dangers of manipulation and misuse of these models has become of paramount importance to public safety and user privacy. In applications such as online content recognition to financial analytics to autonomous vehicles all have shown the be vulnerable to adversaries wishing to manipulate the models or mislead models to their malicious ends. This workshop will focus on recent research and future directions about the security and privacy problems in real-world machine learning systems. We aim to bring together experts from machine learning, security, and privacy communities in an attempt to highlight recent work in these area as well as to clarify the foundations of secure and private machine learning strategies. We seek to come to a consensus on a rigorous framework to formulate adversarial attacks targeting machine learning models, and to characterize the properties that ensure the security and privacy of machine learning systems. Finally, we hope to chart out important directions for future work and cross-community collaborations.



About ICML 2019

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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