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fgsm

Syntologyentry name in harvested coderead from the graph 2026-09-24

fgsm appears in the code Syntology harvested for 14 papers, as 13 distinct code bodies found in 16 places (a place is one code body under one paper). At least one of them ran in 4 of the papers; 1 of the code bodies carries a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named fgsm do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 3 of the 13 distinct code bodies named fgsm; 10 are unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
3ran · our draft was wrong
0ran · fixture could not drive it
0ran
10unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 8 of the 16 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

14 papers shown of 14, newest first; 16 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive; 2 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's code_sha256, Syntology's identity for that exact code: an agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

PaperDateFileStatus SyntologyLicence
Breaking the Barrier: Enhanced Utility and Robustness in Smoothed DRL Agents 26 Jun 2024 trustworthy-ml-lab/robust_highutil_smoothed_drl/SDQN/attacks.py 85a898709da49405 ran · our draft was wrong no licence file found · pointer only
Belief-Enriched Pessimistic Q-Learning against Adversarial State Perturbations 6 Mar 2024 SliencerX/Belief-enriched-robust-Q-learning/attacks.py 85a898709da49405 ran · our draft was wrong no licence file found · pointer only
Proximal Splitting Adversarial Attacks for Semantic Segmentation 14 Jun 2022 jeromerony/alma_prox_segmentation/attacks/fast_gradient.py fa2856a8d3db1703 unverified BSD-3-Clause (permissive)
Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks 26 Oct 2021 RICE-EIC/Robust-Scratch-Ticket/trainers/default.py 6071f5c0529621f1 unverified MIT (permissive)
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning 18 Jun 2021 illidanlab/FedRBN/utils/attack.py e964320f8a75d974 unverified MIT (permissive)
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks 21 Apr 2020 sancharisen/EMPIR/cleverhans/attacks.py 669a4a70d107c4a4 unverified MIT recorded; this copy not marked cleared · pointer only
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks 21 Apr 2020 sancharisen/EMPIR/cleverhans/attacks_tf.py ab300465511282c2 unverified MIT recorded; this copy not marked cleared · pointer only
Adversarial Perturbations Fool Deepfake Detectors 24 Mar 2020 ApGa/adversarial_deepfakes/adv_examples.py 969781f75548142e ran · our draft was wrong MIT (permissive)
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness 2 Mar 2020 Ahmadreza-Jeddi/Learn2Perturb/attacks/fgsm.py fd1f0b10f905c6a8 unverified MIT (permissive)
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers 17 Apr 2019 ameya005/Semantic_Adversarial_Attacks/simple_classifier.py abbf22147fdaa2af unverified MIT (permissive)
Attacking Binarized Neural Networks 1 Nov 2017 AngusG/cleverhans-attacking-bnns/cleverhans/attacks.py 669a4a70d107c4a4 unverified MIT recorded; this copy not marked cleared · pointer only
Attacking Binarized Neural Networks 1 Nov 2017 AngusG/cleverhans-attacking-bnns/cleverhans/attacks_tf.py ab300465511282c2 unverified MIT recorded; this copy not marked cleared · pointer only
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library 3 Oct 2016 johnsonkee/graduate_design/cleverhans/attacks_tf.py 0ea7edb2e2e15307 unverified MIT recorded; this copy not marked cleared · pointer only
Explaining and Harnessing Adversarial Examples 20 Dec 2014 LawrenceMMStewart/Adversarial_Attack/FGSM_LeNet5.py 4cb25a22c32d9e0a ran · our draft was wrong fingerprinted no licence file found · pointer only
arXiv:Wilson_SAFE_Sensitivity-Aware_Features_for_Out-of-Distribution_Object_Detection_ICCV_2023_paper SamWilso/SAFE_Official/SAFE/transforms_detr.py b1bd6764c6d2103a unverified MIT (permissive)
arXiv:Wang_Cant_Slow_Me_Down_Learning_Robust_and_Hardware-Adaptive_Object_Detectors_CVPR_2025_paper Hill-Wu-1998/underload/latency_attack.py d050dc7ddd4e0e36 unverified Apache-2.0 (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the harvest. "Pointer only" means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections