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Register a new userCan Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap?
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Added by
Nathan Inkawhich
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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We design blackbox transfer-based targeted adversarial attacks for an environment where the attackers source model and the target blackbox model may have disjoint label spaces and training datasets. This scenario significantly differs from the standard blackbox setting, and warrants a unique approach to the attacking process. Our methodology begins with the construction of a class correspondence matrix between the whitebox and blackbox label sets. During the online phase of the attack, we then leverage representations of highly related proxy classes from the whitebox distribution to fool the blackbox model into predicting the desired target class. Our attacks are evaluated in three complex and challenging test environments where the source and target models have varying degrees of conceptual overlap amongst their unique categories. Ultimately, we find that it is indeed possible to construct targeted transfer-based adversarial attacks between models that have non-overlapping label spaces! We also analyze the sensitivity of attack success to properties of the clean data. Finally, we show that our transfer attacks serve as powerful adversarial priors when integrated with query-based methods, markedly boosting query efficiency and adversarial success.
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Transfer-based adversarial attacks can effectively evaluate model robustness in the black-box setting. Though several methods have demonstrated impressive transferability of untargeted adversarial examples, targeted adversarial transferability is still challenging. The existing methods either have low targeted transferability or sacrifice computational efficiency. In this paper, we develop a simple yet practical framework to efficiently craft targeted transfer-based adversarial examples. Specifically, we propose a conditional generative attacking model, which can generate the adversarial examples targeted at different classes by simply altering the class embedding and share a single backbone. Extensive experiments demonstrate that our method improves the success rates of targeted black-box attacks by a significant margin over the existing methods -- it reaches an average success rate of 29.6% against six diverse models based only on one substitute white-box model in the standard testing of NeurIPS 2017 competition, which outperforms the state-of-the-art gradient-based attack methods (with an average success rate of $<$2%) by a large margin. Moreover, the proposed method is also more efficient beyond an order of magnitude than gradient-based methods.
We study the optimization problem for decomposing $d$ dimensional fourth-order Tensors with $k$ non-orthogonal components. We derive textit{deterministic} conditions under which such a problem does not have spurious local minima. In particular, we show that if $kappa = frac{lambda_{max}}{lambda_{min}} < frac{5}{4}$, and incoherence coefficient is of the order $O(frac{1}{sqrt{d}})$, then all the local minima are globally optimal. Using standard techniques, these conditions could be easily transformed into conditions that would hold with high probability in high dimensions when the components are generated randomly. Finally, we prove that the tensor power method with deflation and restarts could efficiently extract all the components within a tolerance level $O(kappa sqrt{ktau^3})$ that seems to be the noise floor of non-orthogonal tensor decomposition.
A finite set of integers $A$ is a sum-dominant (also called an More Sums Than Differences or MSTD) set if $|A+A| > |A-A|$. While almost all subsets of ${0, dots, n}$ are not sum-dominant, interestingly a small positive percentage are. We explore sufficient conditions on infinite sets of positive integers such that there are either no sum-dominant subsets, at most finitely many sum-dominant subsets, or infinitely many sum-dominant subsets. In particular, we prove no subset of the Fibonacci numbers is a sum-dominant set, establish conditions such that solutions to a recurrence relation have only finitely many sum-dominant subsets, and show there are infinitely many sum-dominant subsets of the primes.
Adversarial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. However, current studies on adversarial examples focus on supervised learning tasks, relying on the ground-truth data label, a targeted objective, or supervision from a trained classifier. In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation. Our framework exploits a mutual information neural estimator as an information-theoretic similarity measure to generate adversarial examples without supervision. We propose a new MinMax algorithm with provable convergence guarantees for efficient generation of unsupervised adversarial examples. Our framework can also be extended to supervised adversarial examples. When using unsupervised adversarial examples as a simple plug-in data augmentation tool for model retraining, significant improvements are consistently observed across different unsupervised tasks and datasets, including data reconstruction, representation learning, and contrastive learning. Our results show novel methods and advantages in studying and improving robustness of unsupervised learning problems via adversarial examples. Our codes are available at https://github.com/IBM/UAE.
Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.e. given a (possibly corrupted) signal, we wish to recover the latent vector that generated it. Building upon sparse representation theory, we define conditions that are applicable to any inversion algorithm (gradient descent, deep encoder, etc.), under which such generative models are invertible with a unique solution. Importantly, the proposed analysis is applicable to any trained model, and does not depend on Gaussian i.i.d. weights. Furthermore, we introduce two layer-wise inversion pursuit algorithms for trained generative networks of arbitrary depth, and accompany these with recovery guarantees. Finally, we validate our theoretical results numerically and show that our method outperforms gradient descent when inverting such generators, both for clean and corrupted signals.
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