Research map / Adversarial robustness and security / Attacks on text and graphs
Training-Time Attacks against K-nearest Neighbors
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase data dimension | CHOPPA optimal one-point attack on synthetic 1NN · Normal and Uniform synthetic data | attack score: 2.72 -> 10.00 (Normal, ℓ2); 2.70 -> 10.00 (Uniform, ℓ2) | Helped |
| Reduce MNIST dimension to 32 with PCA | GIT²ACHOPPA against 1NN · MNIST | attack effectiveness: decreased ... by between 26% (b = 1) and 12% (b = 20) | Hurt |
| Reduce MNIST dimension to 32 with PCA | 1NN · MNIST hold-out | zero-one loss: 0.102 -> 0.103 | No clear effect |
| Reduce HAPT dimension to 32 with PCA | GIT²ACHOPPA against 1NN · HAPT | attack effectiveness: decreased ... by between 11% and 8% | Hurt |
| Reduce HAPT dimension to 32 with PCA | 1NN · HAPT hold-out | zero-one loss: 0.087 -> 0.111 | Hurt |
| Increase training-set size | CHOPPA optimal one-point attack · Normal synthetic data | attack score | Hurt |
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