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Online content aware scale generator1/16/2024 Content-aware Adversarial Attack Generator (CAG) We demonstrate this with the Content-aware Adversarial Attack Generator (CAG). However, these drawbacks can be overcome. Taking the perspective the attacker, the practical deployment of adversarial attacks suffers from certain drawbacks, namely long adversarial example generating time, high memory cost for launching adversarial attack, insufficient robustness against defense methods and low transferability in black-box attack scenario. For a fuller primer, see Explaining and Harnessing Adversarial Examples (Goodfellow, Shlens, Szegedy). Such vulnerabilities speak to the long road ahead for self-driving systems and other systems where model failure has serious consequences. For example, in adversarial work at UC Berkeley, by adding just a small amount of noise (in the form of spray paint, stickers) on a stop sign, the researchers compromised the output of the DNN to a desired misclassification. Here we will define adversarial attacks as actions, such as peturbations of an image imperceptible to the human eye, causing an intentional misclassification by an AI model. Such stress testing is the core of the field of adversarial robustness. Why are we publishing new methods for attacking AI systems? Because the only way to fortify AI systems is to discover their vulnerabilities.
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