Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data (1806.05476v1)

Abstract: In the past few years, Convolutional Neural Networks (CNNs) have been achieving state-of-the-art performance on a variety of problems. Many companies employ resources and money to generate these models and provide them as an API, therefore it is in their best interest to protect them, i.e., to avoid that someone else copies them. Recent studies revealed that state-of-the-art CNNs are vulnerable to adversarial examples attacks, and this weakness indicates that CNNs do not need to operate in the problem domain (PD). Therefore, we hypothesize that they also do not need to be trained with examples of the PD in order to operate in it. Given these facts, in this paper, we investigate if a target black-box CNN can be copied by persuading it to confess its knowledge through random non-labeled data. The copy is two-fold: i) the target network is queried with random data and its predictions are used to create a fake dataset with the knowledge of the network; and ii) a copycat network is trained with the fake dataset and should be able to achieve similar performance as the target network. This hypothesis was evaluated locally in three problems (facial expression, object, and crosswalk classification) and against a cloud-based API. In the copy attacks, images from both non-problem domain and PD were used. All copycat networks achieved at least 93.7% of the performance of the original models with non-problem domain data, and at least 98.6% using additional data from the PD. Additionally, the copycat CNN successfully copied at least 97.3% of the performance of the Microsoft Azure Emotion API. Our results show that it is possible to create a copycat CNN by simply querying a target network as black-box with random non-labeled data.

• The paper introduces "Copycat CNN", a method to steal knowledge from a target convolutional neural network using minimal labeled data, random non-labeled data, and adversarial examples.
• The method persuades the target model to reveal its learned features, enabling a copycat model to achieve replication accuracy comparable to the original proprietary model.
• This research highlights significant security vulnerabilities in deep neural networks, raising concerns about intellectual property protection and necessitating new defensive strategies against model extraction attacks.

Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data

The paper entitled "Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data" focuses on the vulnerabilities of deep neural networks (DNNs), particularly convolutional neural networks (CNNs), to knowledge extraction attacks. The authors, Jacson Rodrigues Correia-Silva et al., propose a methodology to illicitly replicate the capabilities of a target CNN model using minimal labeled data. They introduce a novel approach termed 'Copycat CNN', which employs non-labeled random data coupled with adversarial examples to coax a target model into revealing its learned features.

Summary of Key Contributions

The research investigates the intellectual property risks associated with DNNs, which are often considered proprietary due to the substantial resources required for their development. The authors introduce the concept that by strategically feeding random, non-labeled data to a model, and augmenting this with adversarial examples, an attacker can infer significant insights into the model's inner workings without needing access to its original training dataset.

This process involves generating adversarial examples that are designed to elicit specific responses from the target network, thereby persuading it to 'confess' details of its learned knowledge. The attacker can then train a secondary model—a 'copycat' CNN—by integrating these inferred insights with the non-labeled random data. Notably, the method achieves substantial replication accuracy, which raises considerable concerns regarding model security and intellectual property protection in commercial environments.

Notable Results

The paper provides numerical results indicating that the Copycat CNN approach yields a replication accuracy comparable to the original model trained with genuine labeled data. These findings underscore the robustness of the attack vector, despite its reliance on random non-labeled data. The empirical results demonstrate that the copycat model can achieve performance metrics that challenge the exclusivity of proprietary models, suggesting potential security implications for sensitive applications of CNNs.

Theoretical and Practical Implications

The theoretical implications of this research challenge the assumption that the onerous demands for labeled datasets are a safeguard against model replication. The paper highlights new dimensions in the adversarial landscape, concerning both the security and ethical considerations of deep learning models. From a practical standpoint, this necessitates a reevaluation of security protocols surrounding the deployment of CNN models, especially in sectors reliant on confidential data and proprietary algorithms.

Speculation on Future Directions

Looking ahead, this paper prompts further exploration into defensive strategies against model extraction attacks. Researchers might investigate improved adversarial training techniques to fortify models against such vulnerabilities or develop framework-level enhancements aimed at safeguarding the internal representations of neural networks. Additionally, exploration of legal and ethical frameworks for AI, addressing model theft and replication, could become increasingly pertinent as these technologies continue to proliferate.

In conclusion, "Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data" offers a critical view into the security shortcomings of CNNs, implying the need for advanced protective measures within AI applications to guard against unauthorized knowledge extraction and intellectual property compromises.