Dissociating spatial frequency reliance from adversarial robustness advantages in neurally guided deep convolutional neural networks
Deep convolutional neural networks (DCNNs) have rivaled humans on many visual tasks, yet they remain vulnerable to near-imperceptible perturbations generated by adversarial attacks. Recent work shows that aligning DCNN representations with human visual cortex activity improves adversarial robustness, but the mechanisms driving this advantage are unclear. One hypothesis suggests that neural alignment confers robustness by biasing models away from brittle high-frequency details and towards the low spatial frequencies (LSF). However, recent work shows that human object recognition critically depends on a narrow, mid-frequency "human channel". Interestingly, this band was partially preserved ...
| Authors | Zhenan Shao, Tianyu Ren, Chengxiao Wang et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 6 May 2026 |
| Last updated at source | 6 May 2026 |
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