Papers › Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks

Exploring the Devil in Graph Spectral Domain for 3D Point Cloud Attacks

15 Feb 2022arXiv:2202.07261archive 2025-07-28

Qianjiang Hu, Daizong Liu, Wei Hu

With the maturity of depth sensors, point clouds have received increasing attention in various applications such as autonomous driving, robotics, surveillance, etc., while deep point cloud learning models have shown to be vulnerable to adversarial attacks. Existing attack methods generally add/delete points or perform point-wise perturbation over point clouds to generate adversarial examples in the data space, which may neglect the geometric characteristics of point clouds. Instead, we propose point cloud attacks from a new perspective -- Graph Spectral Domain Attack (GSDA), aiming to perturb transform coefficients in the graph spectral domain that corresponds to varying certain geometric structure. In particular, we naturally represent a point cloud over a graph, and adaptively transform the coordinates of points into the graph spectral domain via graph Fourier transform (GFT) for compact representation. We then analyze the influence of different spectral bands on the geometric structure of the point cloud, based on which we propose to perturb the GFT coefficients in a learnable manner guided by an energy constraint loss function. Finally, the adversarial point cloud is generated by transforming the perturbed spectral representation back to the data domain via the inverse GFT (IGFT). Experimental results demonstrate the effectiveness of the proposed GSDA in terms of both imperceptibility and attack success rates under a variety of defense strategies. The code is available at https://github.com/WoodwindHu/GSDA.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2202.07261")

Code

Syntology Ran 1 of 10 code samples harvested from 1 repository linked to this paper; 9 have no recorded run. Of those that ran: 1 ran · honoured contract.

By repository: official repository: 10 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

woodwindhu/gsda officialmentioned in paperpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

10 samples harvested; 1 ran; 1 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · honoured contract
9unverified

Licence: 0 of the 10 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from WoodwindHu/GSDA. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “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.

Each sample ends with its 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.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at 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 label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

pc_normalize WoodwindHu/GSDA/distance.py official repository ran · honoured contract fingerprinted MIT (permissive) · ec413739d406e611 · report
array2samples_distance WoodwindHu/GSDA/distance.py official repository unverified MIT (permissive) · 947c203f90fe2111 · report
jitter_input WoodwindHu/GSDA/Lib/utility.py official repository unverified MIT (permissive) · ac3b44ab95cf8165 · report
load_pointcloud WoodwindHu/GSDA/distance.py official repository unverified MIT (permissive) · 0e01a07c931ea6bc · report
norm_l2_loss WoodwindHu/GSDA/Lib/loss_utils.py official repository unverified MIT (permissive) · 702f0f77f4137090 · report
outlier_removal_fn WoodwindHu/GSDA/defense.py official repository unverified MIT (permissive) · 2bddbf49c38dd521 · report
point_removal_fn WoodwindHu/GSDA/defense.py official repository unverified MIT (permissive) · 78c7047c8cc0f3d2 · report
random_drop_fn WoodwindHu/GSDA/defense.py official repository unverified MIT (permissive) · 5a2510c05015f90f · report
set_bn_momentum_default WoodwindHu/GSDA/Model/PointNetPP_ssg.py official repository unverified MIT (permissive) · 0ad12b1e8408e2f9 · report
spectral_loss WoodwindHu/GSDA/Lib/loss_utils.py official repository unverified MIT (permissive) · 9e67731e2a1fd391 · report

Tasks

Autonomous DrivingDenoisingSurface Reconstruction

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

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