Advanced Micro Devices Corporation

Scott A Moe

Sergey Blagodurov

Sudhanva Gurumurthi

Anthony T Gutierrez

Nicholas P Malaya

Jieming Yin

Matthew David Sinclair

Xianwei Zhang

Naman Maheshwari

An apparatus includes one or more processors that are configured to determine a pixel-by-pixel bounds for a perturbed image, generate an adversarial example using an adversarial example generation technique, and modify the adversarial example to generate the perturbed image based on the pixel-by-pixel bounds. When an initial perturbed image does not reside within the pixel-by-pixel bounds, the one or more processors adjust the initial perturbed image to generate the perturbed image by a Weber-Fechner based adversarial perturbation to reside within the pixel-by-pixel bounds. The one or more processors provide the perturbed image to a computing device in an image-based Completely Automated Public Turing Test to tell Computers and Humans Apart (CAPTCHA).

Method and apparatus for generating artificial intelligence resistant verification images

An electronic device includes a controller functional block and a computational functional block. During operation, while the computational functional block executes a test portion of a workload at at least one precision level, the controller functional block monitors a behavior of the computational functional block. Based on the behavior of the computational functional block while executing the test portion of the workload at the at least one precision level, the controller functional block selects a given precision level from among a set of two or more precision levels at which the computational functional block is to execute a remaining portion of the workload. The controller functional block then configures the computational block to execute the remaining portion of the workload at the given precision level.

Selecting a precision level for executing a workload in an electronic device

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Redmond, WA, United States of America

Scott A Moe