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[2410.03952] Pixel-Based Similarities as an Alternative to Neural Data for Improving Convolutional Neural Network Adversarial Robustness
Summary
This paper presents a novel approach to enhancing the adversarial robustness of Convolutional Neural Networks (CNNs) by utilizing pixel-based similarities instead of neural data, maintaining effectiveness while simplifying implementation.
Why It Matters
The study addresses a critical challenge in machine learning—adversarial attacks on CNNs—by proposing a more accessible method for improving robustness. This could facilitate broader adoption of robust AI systems without the need for specialized neural data, making advancements in AI safety more achievable.
Key Takeaways
- Introduces a pixel-based similarity method to enhance CNN robustness.
- Eliminates the need for specialized neural recordings, simplifying implementation.
- Maintains effectiveness comparable to previous methods using neural data.
- Highlights the potential for integrating brain-inspired principles in AI.
- Encourages further exploration of lightweight methods for robust AI systems.
Computer Science > Machine Learning arXiv:2410.03952 (cs) [Submitted on 4 Oct 2024 (v1), last revised 12 Feb 2026 (this version, v3)] Title:Pixel-Based Similarities as an Alternative to Neural Data for Improving Convolutional Neural Network Adversarial Robustness Authors:Elie Attias, Cengiz Pehlevan, Dina Obeid View a PDF of the paper titled Pixel-Based Similarities as an Alternative to Neural Data for Improving Convolutional Neural Network Adversarial Robustness, by Elie Attias and 2 other authors View PDF HTML (experimental) Abstract:Convolutional Neural Networks (CNNs) excel in many visual tasks but remain susceptible to adversarial attacks-imperceptible perturbations that degrade performance. Prior research reveals that brain-inspired regularizers, derived from neural recordings, can bolster CNN robustness; however, reliance on specialized data limits practical adoption. We revisit a regularizer proposed by Li et al. (2019) that aligns CNN representations with neural representational similarity structures and introduce a data-driven variant. Instead of a neural recording-based similarity, our method computes a pixel-based similarity directly from images. This substitution retains the original biologically motivated loss formulation, preserving its robustness benefits while removing the need for neural measurements or task-specific augmentations. Notably, this data-driven variant provides the same robustness improvements observed with neural data. Our approach is lightw...
