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Lateralized Approach for Robustness Against Attacks in Emotion Categorization from Images

EasyChair Preprint no. 5296

16 pagesDate: April 6, 2021


Deep learning has achieved a high classification accuracy on image classification tasks, including emotion categorization. However, deep learning models are highly vulnerable to adversarial attacks. Even a small change, imperceptible to a human (e.g. one-pixel attack), can decrease the classification accuracy of deep models. One reason could be their homogeneous representation of knowledge that considers all pixels in an image to be equally important is easily fooled. Enabling multiple representations of the same object, e.g. at the constituent and holistic viewpoints provides robustness against attacking a single view. This heterogeneity is provided by lateralization in biological systems. Lateral asymmetry of biological intelligence suggests heterogeneous learning of objects. This heterogeneity allows information to be learned at different levels of abstraction, i.e. at the constituent and the holistic level, enabling multiple representations of the same object. This work aims to create a novel system that can consider heterogeneous features e.g. mouth, eyes, nose, and jaw in a face image for emotion categorization. The experimental results show that the lateralized system successfully considers constituent and holistic features to exhibit robustness to unimportant and irrelevant changes in an image, demonstrating performance accuracy better than (or similar) to the deep learning system (VGG19). Overall, the novel lateralized method shows a stronger resistance to changes (10.86% - 47.72% decrease) than the deep model (25.15% - 83.43% decrease). The advances arise by allowing heterogeneous features, which enable constituent and holistic representations of image components.

Keyphrases: adversarial attacks, emotion categorization, facial expression, lateralization, Learning Classifier Systems (LCS), sUpervised Classifier System (UCS), VGG19

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Harisu Abdullahi Shehu and Abubakar Siddique and Will Browne and Hedwig Eisenbarth},
  title = {Lateralized Approach for Robustness Against Attacks in Emotion Categorization from Images},
  howpublished = {EasyChair Preprint no. 5296},

  year = {EasyChair, 2021}}
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