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 IDTEC-6.1
Paper Title REGULARIZED INTERMEDIATE LAYERS ATTACK: ADVERSARIAL EXAMPLES WITH HIGH TRANSFERABILITY
Authors Xiaorui Li, Weiyu Cui, Jiawei Huang, Wenyi Wang, Jianwen Chen, University of Electronic Science and Technology of China, China
SessionTEC-6: Image and Video Processing 2
LocationArea G
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Processing: Linear and nonlinear filtering of images & video
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Although convolutional neural network (CNN) models have demonstrated state-of-the-art performance, especially in many image classification and recognition tasks, the classification accuracy would significantly decreased in the adversarial image samples set by adding slight perturbations in the input images. Currently, many adversarial examples were designed for specific CNNs but they were not universal valid across different CNNs. In this paper, we proposed a new intermediate layer optimization method to ensure that the adversarial examples are effective across different CNN models. Given one image, the proposed algorithm can derive multiple adversarial examples from just one white-box adversarial example by analyzing its regularized features in the intermediate layers of the attacked CNN. The adversarial examples derived from the intermediate layers showed better transferability compared with the original white-box adversarial example. According to the experiments on multiple CNN models, our algorithm promotes the averaging transfer attacking success rate (ASR) by 10.5% and 4.81%, compared to the baseline white-box attacking methods and the recent intermediate layer based attacking method ILA respectively.