Mopuri, KR and Uppala, PK and Babu, RV (2018) Ask, acquire, and attack: data-free UAP generation using class impressions. In: 15th European Conference on Computer Vision, ECCV 2018, 8 - 14 September 2018, Munich, pp. 20-35.
Full text not available from this repository.Abstract
Deep learning models are susceptible to input specific noise, called adversarial perturbations. Moreover, there exist input-agnostic noise, called Universal Adversarial Perturbations (UAP) that can affect inference of the models over most input samples. Given a model, there exist broadly two approaches to craft UAPs: (i) data-driven: that require data, and (ii) data-free: that do not require data samples. Data-driven approaches require actual samples from the underlying data distribution and craft UAPs with high success (fooling) rate. However, data-free approaches craft UAPs without utilizing any data samples and therefore result in lesser success rates. In this paper, for data-free scenarios, we propose a novel approach that emulates the effect of data samples with class impressions in order to craft UAPs using data-driven objectives. Class impression for a given pair of category and model is a generic representation (in the input space) of the samples belonging to that category. Further, we present a neural network based generative model that utilizes the acquired class impressions to learn crafting UAPs. Experimental evaluation demonstrates that the learned generative model, (i) readily crafts UAPs via simple feed-forwarding through neural network layers, and (ii) achieves state-of-the-art success rates for data-free scenario and closer to that for data-driven setting without actually utilizing any data samples.
| Item Type: | Conference Paper |
|---|---|
| Publication: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Publisher: | Springer Verlag |
| Additional Information: | The copyright for this article belongs to the Authors. |
| Keywords: | Computer vision; Deep learning, Adversarial attacks; Class impressions; Data-free attacks; Image-agnostic perturbations; Ml systems, Network layers |
| Department/Centre: | Division of Interdisciplinary Sciences > Computational and Data Sciences |
| Date Deposited: | 02 Sep 2022 05:42 |
| Last Modified: | 02 Sep 2022 05:42 |
| URI: | https://eprints.iisc.ac.in/id/eprint/76360 |
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