Gradient-based adversarial attacks on categorical sequence models via traversing an embedded world
Deep learning models suffer from a phenomenon called adversarial attacks: we can apply minor changes to the model input to fool a classifier for a particular example. The literature mostly considers adversarial attacks on models with images and other structured inputs. However, the adversarial attacks for categorical sequences can also be harmful. Successful attacks for inputs in the form of categorical sequences should address the following challenges: (1) non-differentiability of the target function, (2) constraints on transformations of initial sequences, and (3) diversity of possible problems. We handle these challenges using two black-box adversarial attacks. The first approach adopts a Monte-Carlo method and allows usage in any scenario, the second approach uses a continuous relaxation of models and target metrics, and thus allows usage of state-of-the-art methods for adversarial attacks with little additional effort. Results for money transactions, medical fraud, and NLP datasets suggest that proposed methods generate reasonable adversarial sequences that are close to original ones but fool machine learning models.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackDiversitySimilar Papers 제목 키워드 기반
Differentiable Language Model Adversarial Attacks on Categorical Sequence Classifiers
An adversarial attack paradigm explores various scenarios for the vulnerability of deep learning models: minor changes of the input can force a model failure. Most of the state of the art frameworks focus on adversarial …
Adversarial AttackLanguage ModelingLanguage ModellingProbabilistic Categorical Adversarial Attack & Adversarial Training
The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. However, how to generate adversarial examples with categorical data is an important prob…
Adversarial AttackBOSH: An Efficient Meta Algorithm for Decision-based Attacks
Adversarial example generation becomes a viable method for evaluating the robustness of a machine learning model. In this paper, we consider hard-label black-box attacks (a.k.a. decision-based attacks), which is a challe…
Adversarial AttackBayesian OptimizationAlgebraic Adversarial Attacks on Integrated Gradients
Adversarial attacks on explainability models have drastic consequences when explanations are used to understand the reasoning of neural networks in safety critical systems. Path methods are one such class of attribution …
Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
Adversarial attacks on tabular data present fundamental challenges distinct from image or text domains due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where pixel perturbations …