Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO

• URL: http://arxiv.org/abs/2206.06761v1
• Date: Tue, 14 Jun 2022 11:20:16 GMT
• Title: Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO
• Authors: Javier Rando and Nasib Naimi and Thomas Baumann and Max Mathys
• Abstract summary: This work conducts the first analysis on the robustness against adversarial attacks on self-supervised Vision Transformers trained using DINO. First, we evaluate whether features learned through self-supervision are more robust to adversarial attacks than those emerging from supervised learning. Then, we present properties arising for attacks in the latent space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work conducts the first analysis on the robustness against adversarial attacks on self-supervised Vision Transformers trained using DINO. First, we evaluate whether features learned through self-supervision are more robust to adversarial attacks than those emerging from supervised learning. Then, we present properties arising for attacks in the latent space. Finally, we evaluate whether three well-known defense strategies can increase adversarial robustness in downstream tasks by only fine-tuning the classification head to provide robustness even in view of limited compute resources. These defense strategies are: Adversarial Training, Ensemble Adversarial Training and Ensemble of Specialized Networks.

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