Adversarial Reprogramming of Neural Networks

Abstract

Deep neural networks are susceptible to adversarial attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as confusing a cat with a computer. Previous adversarial attacks have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker. We introduce attacks that instead reprogram the target model to perform a task chosen by the attacker without the attacker needing to specify or compute the desired output for each test-time input. This attack finds a single adversarial perturbation, that can be added to all test-time inputs to a machine learning model in order to cause the model to perform a task chosen by the adversary-even if the model was not trained to do this task. These perturbations can thus be considered a program for the new task. We demonstrate adversarial reprogramming on six ImageNet classification models, repurposing these models to perform a counting task, as well as classification tasks: classification of MNIST and CIFAR-10 examples presented as inputs to theImageNet model

Date
Jan 27, 2020 12:00 PM
Ramesh Kumar Sah
Ramesh Kumar Sah
Research Assistant

Hi, I am a PhD student in the Embedded and Pervasive System Laboratory (EPSL) at Washington State University, Pullman. I work with Dr. Hassan Ghasemzadeh in areas such as robust machine learning and adversarial methods in wearable sensor systems.