Adversarial Reprogramming of Neural Networks (1806.11146v2)

Abstract: Deep neural networks are susceptible to \emph{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 {\em 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 the ImageNet model.

• The paper introduces adversarial reprogramming to repurpose pre-trained models by applying a uniform perturbation across all inputs.
• It achieves high accuracy, with MNIST classifications reaching around 97% and CIFAR-10 models exceeding 69%, proving model versatility.
• The study underscores significant security implications and challenges in protecting neural networks from unintended repurposing.

Adversarial Reprogramming of Neural Networks: A Detailed Examination

The paper "Adversarial Reprogramming of Neural Networks" by Gamaleldin F. Elsayed, Ian Goodfellow, and Jascha Sohl-Dickstein proposes an innovative attack framework that extends traditional adversarial examples by allowing existing neural networks (NNs) to be reprogrammed to perform entirely different tasks. This form of attack doesn't merely aim to produce erroneous outputs from minor perturbations but rather seeks to redirect the entire functionality of a model without retraining its internal weights. This marks a sophisticated evolution in the scope of adversarial attacks, characterized by repurposing resources in pre-trained models to execute tasks they were not initially deployed to handle.

Key Contributions and Methodology

The central thesis of the paper is to showcase how adversarial reprogramming can be achieved through the application of a single adversarial perturbation applied uniformly across all test inputs. The perturbation acts as a 'program' instructing the neural network to conduct a new task, irrespective of its original design. The authors illustrate their approach effectively by demonstrating adversarial reprogramming on six distinct ImageNet models, repurposing them to solve various tasks, including: counting visual squares, performing classifications on MNIST, and distinguishing CIFAR-10 datasets — all without alterations to the network's architecture or parameters.

The adversarial program is realized as an additive contribution incorporated into the network's input. This strategic adversarial aim is accomplished by applying consistent transformations via functions that map adversarial task inputs into valid inputs for the original task's neural network and vice versa for outputs. Notably, the adversarial transformations need not be imperceptibly small, which sets them apart from classical adversarial examples and broadens the horizon of potential applications.

Empirical Evaluation

The paper delivers compelling experimental results that reinforce the practicality of the approach. They successfully repurposed ImageNet classification models to function as MNIST and CIFAR-10 classifiers, as well as solve counting problems. The reported precision in these tasks was notably high, with MNIST classifications achieving around 97% test accuracy on several models and CIFAR-10 classifications reaching above 69% for Inception models. These outcomes indicate that even sophisticated and deep architectures are not immune to this form of adversarial reprogramming.

An intriguing point is the consistent observation that neural networks retain substantial flexibility for new tasks if precursory training has been conducted, compared to their randomly-initialized counterparts. This suggests that pretrained networks employ versatile internal representations, making them more susceptible to reprogramming attacks.

Implications and Future Directions

The acknowledgment of the potential for adversarial reprogramming has vital implications for the security and design of machine learning systems. Practically, it indicates that without adequate countermeasures, neural networks deployed in real-world applications could potentially be commandeered to perform unintended tasks, making them plausible subjects for computational theft or misuse. Exploring defenses against this type of attack must, therefore, consider patterns that can preemptively block or detect malicious inputs aiming at reprogramming neural functionalities.

From a theoretical standpoint, adversarial reprogramming insinuates broad versatility within pre-trained networks and their latent capacities to generalize across domains with differing datasets. It poses a fascinating question about the limits of neural network flexibility and suggests a rich avenue for future work, particularly in examining whether domains such as audio, video, and text are similarly vulnerable and can be harnessed positively in adaptive ML applications.

In ambits like dynamic neural architectures or models with inherent memory and focus attributes, such as RNNs with attention mechanisms, further exploration could reveal additional layers of programmability. This underlines the utility of studying adversarial interactions not only as threats but as potential advantages in repurposing AI technologies efficiently and responsibly.