Linyi Li, Bo Li, Xi Xiao, Minhui Xue, Yuxin Cao, Youwei Shu, Derui Wang, Siji Chen
We lifted 20 functions out of this paper's own repositories and ran 19 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| tdano1/eg-on-smoothing | canonical | 19 of 20 |
| Function | Status | Where it lives |
|---|---|---|
| Int_for_gamma | Ran | tdano1/eg-on-smoothing/distribution.py pointer only (licence: NONE) · get_code("ecc60901ba340fb0") |
| T | Ran | tdano1/eg-on-smoothing/th_heuristic.py pointer only (licence: NONE) · get_code("46f3c7ec743814b7") |
| confidence_bound | Ran | tdano1/eg-on-smoothing/smooth.py pointer only (licence: NONE) · get_code("2f3ad7fc799ce527") |
| confint | Ran | tdano1/eg-on-smoothing/distribution.py pointer only (licence: NONE) · get_code("9a960e95cfbfcce0") |
| conv3x3 | Ran | tdano1/eg-on-smoothing/archs/cifar_resnet.py pointer only (licence: NONE) · get_code("fac5364e2f53c6db") |
| conv3x3 | Ran | tdano1/eg-on-smoothing/archs/wide_resnet_imagenet64.py pointer only (licence: NONE) · get_code("00e569acd6b45ef0") |
| fast_beta | Ran | tdano1/eg-on-smoothing/algo/algo.py pointer only (licence: NONE) · get_code("cc016e2d63decb65") |
| get_beta | Ran | tdano1/eg-on-smoothing/th_heuristic.py pointer only (licence: NONE) · get_code("2a24fb5dc7d5592e") |
| get_beta2 | Ran | tdano1/eg-on-smoothing/th_heuristic.py pointer only (licence: NONE) · get_code("fadbf4d5b828f448") |
| get_input_shape | Ran | tdano1/eg-on-smoothing/datasets.py pointer only (licence: NONE) · get_code("42a362aee3014e4b") |
| get_num_classes | Ran | tdano1/eg-on-smoothing/datasets.py pointer only (licence: NONE) · get_code("d11c42096411b81b") |
| lambertWlog | Ran | tdano1/eg-on-smoothing/utils.py pointer only (licence: NONE) · get_code("15c496ab663c5ec4") |
| ln_exp_plus_exp | Ran | tdano1/eg-on-smoothing/algo/algo.py pointer only (licence: NONE) · get_code("431e5dacd9481349") |
| read_orig_Rs | Ran | tdano1/eg-on-smoothing/utils.py pointer only (licence: NONE) · get_code("27a5ef2a70140e8c") |
| read_pAs | Ran | tdano1/eg-on-smoothing/utils.py pointer only (licence: NONE) · get_code("51b78b0b7766cfbe") |
| sum_exp_greater_than_one | Ran | tdano1/eg-on-smoothing/algo/algo.py pointer only (licence: NONE) · get_code("781b5c0335c1dea9") |
| u2x | Ran | tdano1/eg-on-smoothing/distribution.py pointer only (licence: NONE) · get_code("72bd6ca75caa17d8") |
| wide_resnet_imagenet64 | Ran | tdano1/eg-on-smoothing/archs/wide_resnet_imagenet64.py pointer only (licence: NONE) · get_code("36e1813d89519d14") |
| wide_resnet_imagenet64_1000class | Ran | tdano1/eg-on-smoothing/archs/wide_resnet_imagenet64.py pointer only (licence: NONE) · get_code("75a74c940c2e498e") |
| get_dataset | Not yet run | tdano1/eg-on-smoothing/datasets.py pointer only (licence: NONE) · get_code("ebd680ff3d6e442b") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Randomized Smoothing (RS) is currently a scalable certified defense method providing robustness certification against adversarial examples. Although significant progress has been achieved in providing defenses against ℓ p adversaries, the interaction between the smoothing distribution and the robustness certification still remains vague. In this work, we comprehensively study the effect of two families of distributions, named Exponential Standard Gaussian (ESG) and Exponential General Gaussian (EGG) distributions, on Randomized Smoothing and Double Sampling Randomized Smoothing (DSRS). We derive an analytic formula for ESG's certified radius, which converges to the origin formula of RS as the dimension d increases. Additionally, we prove that EGG can provide tighter constant factors than DSRS in providing Ω( √ d) lower bounds of ℓ 2 certified radius, and thus further addresses the curse of dimensionality in RS. Our experiments on realworld datasets confirm our theoretical analysis of the ESG distributions, that they provide almost the same certification under different exponents η for both RS and DSRS. In addition, EGG brings a significant improvement to the DSRS certification, but the mechanism can be different when the classifier properties are different. Compared to the primitive DSRS, the increase in certified accuracy provided by EGG is prominent, up to 6.4% on Im-ageNet. Our code is available at https://gi thub.com/tdano1/eg-on-smoothing.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2406.02309")
get_code_for_paper("2406.02309")
have("2406.02309")
Connect an agent — have() is free.