A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots
Boyang Zhang, Xinlei He, Yun Shen, Tianhao Wang, Yang Zhang

TL;DR
This paper uncovers a new side channel attack that uses scientific plots to steal information about machine learning models, demonstrating the attack's effectiveness and proposing potential defenses.
Contribution
It introduces a novel attack method leveraging scientific plots for model information theft and evaluates its effectiveness across multiple datasets.
Findings
The attack accurately infers model architecture and hyperparameters from plots.
Scientific plots' shape is the main factor enabling the attack.
Proposed defenses can reduce attack success but are still bypassable.
Abstract
Building advanced machine learning (ML) models requires expert knowledge and many trials to discover the best architecture and hyperparameter settings. Previous work demonstrates that model information can be leveraged to assist other attacks, such as membership inference, generating adversarial examples. Therefore, such information, e.g., hyperparameters, should be kept confidential. It is well known that an adversary can leverage a target ML model's output to steal the model's information. In this paper, we discover a new side channel for model information stealing attacks, i.e., models' scientific plots which are extensively used to demonstrate model performance and are easily accessible. Our attack is simple and straightforward. We leverage the shadow model training techniques to generate training data for the attack model which is essentially an image classifier. Extensive…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Network Security and Intrusion Detection
