Simple Black-Box Adversarial Perturbations for Deep Networks (1612.06299v1)

Abstract: Deep neural networks are powerful and popular learning models that achieve state-of-the-art pattern recognition performance on many computer vision, speech, and language processing tasks. However, these networks have also been shown susceptible to carefully crafted adversarial perturbations which force misclassification of the inputs. Adversarial examples enable adversaries to subvert the expected system behavior leading to undesired consequences and could pose a security risk when these systems are deployed in the real world. In this work, we focus on deep convolutional neural networks and demonstrate that adversaries can easily craft adversarial examples even without any internal knowledge of the target network. Our attacks treat the network as an oracle (black-box) and only assume that the output of the network can be observed on the probed inputs. Our first attack is based on a simple idea of adding perturbation to a randomly selected single pixel or a small set of them. We then improve the effectiveness of this attack by carefully constructing a small set of pixels to perturb by using the idea of greedy local-search. Our proposed attacks also naturally extend to a stronger notion of misclassification. Our extensive experimental results illustrate that even these elementary attacks can reveal a deep neural network's vulnerabilities. The simplicity and effectiveness of our proposed schemes mean that they could serve as a litmus test for designing robust networks.

• The paper demonstrates a novel technique that generates adversarial examples using only network output queries without internal access.
• It employs a greedy local-search method to iteratively perturb key pixels, causing significant misclassifications in deep networks.
• Experimental results on datasets like CIFAR10 and MNIST confirm high success rates with minimal pixel changes compared to traditional attacks.

An Academic Overview of "Simple Black-Box Adversarial Perturbations for Deep Networks"

The paper entitled "Simple Black-Box Adversarial Perturbations for Deep Networks" by Nina Narodytska and Shiva Prasad Kasiviswanathan addresses the susceptibility of deep neural networks, particularly convolutional neural networks (CNNs), to adversarial perturbations. This work explores the black-box model where an adversary can create adversarial examples without any prior knowledge of the network's internal parameters or architecture. This essay will elaborate on the methodologies, findings, and implications presented in the paper.

Key Contributions and Methodologies

1. Adversarial Perturbations Without Network Knowledge: The paper introduces a simple yet effective technique to generate adversarial perturbations in a black-box setting. Unlike prior methods that required access to the neural network's internal details, this approach leverages the network’s output predictions to design perturbations. It models the network as an oracle, only requiring the adversary to observe the network's classification results.
2. Single Pixel Manipulation and Greedy Local Search: Initially, the paper investigates the impact of perturbing a single pixel or a small set of pixels. Surprisingly, such minimal manipulations can often result in significant misclassification. Building on this observation, the authors introduce a greedy local-search method. This adaptive approach selects a small set of pixels to perturb iteratively, optimizing the adversarial image generation process without exceeding the valid pixel range of the input image.
3. Experimental Insights: Through extensive experimentation on well-established datasets such as CIFAR10, MNIST, STL10, and ImageNet1000, the paper demonstrates that the proposed local-search approach effectively bypasses the network defenses with minimal perturbations. Notably, the approach consistently succeeds in generating adversarial images by altering a surprisingly small fraction of the image pixels.

Experimental Results

• The local-search based attack achieves high success rates across different datasets and model architectures, including Network-in-Network and VGG models.
• The attack requires significantly less perturbation than traditional methods like the fast-gradient sign method, while also needing fewer pixel alterations, making it computationally efficient.

Implications and Future Directions

The results unveil critical vulnerabilities in CNNs within the field of adversarial learning, emphasizing the importance of developing robust defenses against potential threats. The black-box framework employed here portrays a more realistic adversarial threat scenario compared to white-box approaches, bridging the gap between theoretical advancements and practical security concerns.

The simplicity and efficacy of this method also indicate its utility as a fundamental tool for evaluating and improving the robustness of neural networks. Furthermore, the insights derived from this paper could lead to the development of more agile defense mechanisms, potentially incorporating techniques such as dynamic adversarial training or gradient obfuscation.

In conclusion, this research not only highlights the ease with which existing neural network models can be compromised but also paves the way for secure real-world deployment by providing a basis for future advances in defensive strategies. The exploration of black-box adversarial attacks continues to be a critical area of focus in ensuring the integrity and reliability of AI systems.