Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
(2402.17390)Abstract
Machine-learning models demand for periodic updates to improve their average accuracy, exploiting novel architectures and additional data. However, a newly-updated model may commit mistakes that the previous model did not make. Such misclassifications are referred to as negative flips, and experienced by users as a regression of performance. In this work, we show that this problem also affects robustness to adversarial examples, thereby hindering the development of secure model update practices. In particular, when updating a model to improve its adversarial robustness, some previously-ineffective adversarial examples may become misclassified, causing a regression in the perceived security of the system. We propose a novel technique, named robustness-congruent adversarial training, to address this issue. It amounts to fine-tuning a model with adversarial training, while constraining it to retain higher robustness on the adversarial examples that were correctly classified before the update. We show that our algorithm and, more generally, learning with non-regression constraints, provides a theoretically-grounded framework to train consistent estimators. Our experiments on robust models for computer vision confirm that (i) both accuracy and robustness, even if improved after model update, can be affected by negative flips, and (ii) our robustness-congruent adversarial training can mitigate the problem, outperforming competing baseline methods.
Overview
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The paper introduces the concept of 'robustness negative flips' (RNFs), which occur when updated machine learning models become more susceptible to adversarial attacks that previously had no effect.
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It proposes a novel methodology called Robustness-Congruent Adversarial Training (RCAT), designed to minimize both accuracy and adversarial robustness regressions during model updates.
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Through empirical evaluation on image classification models, RCAT demonstrated superior capability to balance minimizing 'negative flips' (NFs) and RNFs compared to existing methods.
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The work highlights the importance of considering both accuracy and adversarial robustness in evaluating machine learning model updates, suggesting further research into loss functions and regularizers.
Evaluating and Mitigating Regression in Secure Machine Learning Model Updates
Introduction
The recent advancements in ML have necessitated the frequent updating of ML models to leverage novel architectures and additional data for improved performance. Particularly in applications like cybersecurity and image tagging, where data distributions and threat landscapes evolve rapidly, model updates are crucial for maintaining high detection accuracies. However, this practice of model updating introduces a unique challenge; it can lead to what is termed as "negative flips" (NFs) - instances where the updated model misclassifies samples that were previously correctly classified by the older version, essentially causing a regression in model performance as perceived by end-users. Building on the concept of NFs, this work sheds light on another critical dimension of regression concerning adversarial robustness, introducing "robustness negative flips" (RNFs). RNFs occur when adversarial examples, previously ineffective against the old model, successfully deceive the updated model, thereby regressing its perceived security.
Regression in Machine Learning Models
While the phenomenon of accuracy regression, quantified through NFs, has been acknowledged and addressed in the literature, the regression of adversarial robustness or RNFs has not been thoroughly investigated. Adversarial robustness, the model's resilience against maliciously crafted inputs, is a vital aspect of secure ML applications. The work identifies that similar to NFs, updating models can also enhance vulnerability to adversarial attacks, a situation undesirably analogous to software updates that introduce new bugs while fixing old ones.
Robustness-Congruent Adversarial Training (RCAT)
To tackle this dual-regression problem, the authors propose a novel methodology named Robustness-Congruent Adversarial Training (RCAT). At its core, RCAT is an extension of adversarial training that not only seeks to improve the model's robustness but does so with a constraint: minimizing RNFs alongside NFs. By incorporating an additional non-regression penalty term into the optimization problem, RCAT ensures that the updated model does not compromise on adversarial examples previously handled by its predecessor. The authors theoretically establish that this learning framework with non-regression constraints yields a statistically consistent estimator, a crucial attribute for ensuring that the updated model learns the underlying true distribution without compromising convergence rates.
Empirical Evaluation on Image Classification Models
The empirical analysis focuses on robust models designed for image classification tasks, where frequent updates are common to maintain system performance. Through exhaustive experiments involving various model updates, the authors demonstrate the existence and impact of RNFs in practice. They compare RCAT with existing methodologies like Positive-Congruent Training (PCT) and its robust extension (PCAT), showcasing RCAT's superior ability to balance the trade-off between minimizing NFs and RNFs. This balance is critical for updates where an improvement in average accuracy or robustness does not guarantee enhanced security against adversarial attacks.
Implications and Future Directions
This work opens up a new avenue in the research of secure ML model updates by highlighting the overlooked aspect of robustness regression. The findings emphasize the need for a holistic evaluation of model updates, considering both accuracy and adversarial robustness regressions. As future work, the authors suggest exploring different loss functions and regularizers to further mitigate regressions and extending the RCAT methodology to other domains where model updates are frequent and critical for performance maintenance.
In conclusion, the study underscores the importance of cautious and informed model updating processes in ML applications, especially those requiring high levels of security against adversarial threats. By introducing RCAT, the authors provide a pioneering solution for reducing regressions, paving the way for more secure and reliable ML model updates.
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