2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-IP-5.11
Paper Title SQUEEZE AND RECONSTRUCT: IMPROVED PRACTICAL ADVERSARIAL DEFENSE USING PAIRED IMAGE COMPRESSION AND RECONSTRUCTION
Authors Bo-Han Kung, Pin-Chun Chen, Yu-Cheng Liu, Jun-Cheng Chen, Research Center for Information Technology Innovation, Academia Sinica, Taiwan
SessionMLR-APPL-IP-5: Machine learning for image processing 5
LocationArea E
Session Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Time:Tuesday, 21 September, 13:30 - 15:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image processing
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract As shown in the previous literature, non-robust features of an image such as texture are both the secrets why deep neural networks achieve outstanding classification performance and the sources of adversarial examples. Image compression methods such as JPEG can be used to effectively defend against diverse adversarial attacks by eliminating these non-robust features in the pre-processing stage while significantly sacrificing clean accuracy. To address this issue, we present a squeeze-and-reconstruct framework which first performs image compression followed by image reconstruction to recover necessary details for the improved clean and robust accuracies. With extensive experiments on the challenging ImageNet dataset, the evaluation results demonstrate the effectiveness of the proposed method to defend against the Fast Gradient Sign Method and the powerful Projected Gradient Descent attacks in the white-box scenarios. In addition, the proposed approach also outperforms other common and off-the-shelf defense models in terms of both clean and robust accuracies.