RT Conference Proceedings
T1 Plausible Counterfactuals: 												Auditing Deep Learning Classifiers with Realistic Adversarial Examples
A1 Barredo-Arrieta, Alejandro
A1 Del Ser, Javier
AB The last decade has witnessed the proliferation of Deep Learning models in many applications, achieving unrivaled levels of predictive performance. Unfortunately, the black-box nature of Deep Learning models has posed unanswered questions about what they learn from data. Certain application scenarios have highlighted the importance of assessing the bounds under which Deep Learning models operate, a problem addressed by using assorted approaches aimed at audiences from different domains. However, as the focus of the application is placed more on non-expert users, it results mandatory to provide the means for him/her to trust the model, just like a human gets familiar with a system or process: by understanding the hypothetical circumstances under which it fails. This is indeed the angular stone for this research work: to undertake an adversarial analysis of a Deep Learning model. The proposed framework constructs counterfactual examples by ensuring their plausibility, e.g. there is a reasonable probability that a human could generate them without resorting to a computer program. Therefore, this work must be regarded as valuable auditing exercise of the usable bounds a certain model is constrained within, thereby allowing for a much greater understanding of the capabilities and pitfalls of a model used in a real application. To this end, a Generative Adversarial Network (GAN) and multi-objective heuristics are used to furnish a plausible attack to the audited model, efficiently trading between the confusion of this model, the intensity and plausibility of the generated counterfactual. Its utility is showcased within a human face classification task, unveiling the enormous potential of the proposed framework.
PB Institute of Electrical and Electronics Engineers Inc.
SN 9781728169262
YR 2020
FD 2020-07
LA eng
NO Barredo-Arrieta , A & Del Ser , J 2020 , Plausible Counterfactuals : Auditing Deep Learning Classifiers with Realistic Adversarial Examples . in 2020 International Joint Conference on Neural Networks, IJCNN 2020 - Proceedings . , 9206728 , Proceedings of the International Joint Conference on Neural Networks , Institute of Electrical and Electronics Engineers Inc. , 2020 International Joint Conference on Neural Networks, IJCNN 2020 , Virtual, Glasgow , United Kingdom , 19/07/20 . https://doi.org/10.1109/IJCNN48605.2020.9206728
NO conference
NO Publisher Copyright: © 2020 IEEE.
NO The authors would like to thank the Basque Government for its support through the EMAITEK and ELKARTEK funding programs. Javier Del Ser also receives support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government.
DS TECNALIA Publications
RD 1 sept 2024