Research map / Adversarial robustness and security / Certified and empirical robustness
Adversarial Robustness of Supervised Sparse Coding
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the Lasso regularization parameter from λ=0.2 to λ=0.3 (ablation) | Supervised sparse coding · MNIST | certified radius | Helped |
| Increase the Lasso regularization parameter from λ=0.2 to λ=0.3 (ablation) | Supervised sparse coding · MNIST | risk | Hurt |
| Use the supervised sparse coding certificate instead of randomized smoothing | Certified robustness on MNIST · MNIST | certified accuracy | No clear effect |
| Apply projected gradient descent attacks with increasing energy budget | Supervised sparse coding classifier · Synthetic separable distribution | accuracy under attack | No clear effect |
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