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The phrase "adversarial attacks" is correct and usable in written English.
It is typically used in the context of machine learning and cybersecurity to refer to techniques that attempt to deceive or manipulate models or systems.
Example: "Researchers are increasingly focused on developing robust algorithms to defend against adversarial attacks that can compromise the integrity of AI systems."
Alternatives: "malicious inputs" or "deceptive inputs".
Exact(45)
Defending against whitebox adversarial attacks via randomized discretization.
Dec 14: Recent progress in making deep nets resistant to adversarial attacks.
Toward deep learning models resistant to adversarial attacks by Madry, Makelov, Schmidt, Tsipras, Vladu.
Claire Tomlin, Qie Hu. "Secure Estimation based Kalman Filter for Cyber-Physical Systems against Adversarial Attacks".
Finlayson, S. et al. Adversarial attacks against medical deep learning systems.
Thus, even more recent and sophisticated adversarial attacks are susceptible to human deciphering.
Similar(15)
Experiment 6 tested human observers on images produced by a state-of-the-art "localized" adversarial attack ("LaVAN 13).
Recently, a creative and sophisticated adversarial attack has allowed for the creation not only of adversarial 2D images but also adversarial 3D objects that machines reliably misclassify12.
Several recent research results describe how to design Distributed Hash Tables (DHTs) that are robust to adversarial attack via Byzantine faults.
(Indeed, the creators of this adversarial attack informally noted this resemblance in later work26). What about truly bizarre images that are considered "totally unrecognizable to human eyes"14?
Even for this advanced adversarial attack, human responses aligned with the machine's: 87% of subjects identified the machine's classifications at above-chance rates, and 100% of the images showed above-chance human-machine agreement (Fig. 4c).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com