Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Disrupting adversarial transferability in deep neural networks

Patterns(2022)10.1016/j.patter.2022.100472Source: DataRank Database

Disrupting adversarial transferability in deep neural networks is a research paper published in Patterns (2022). On theSindex it has a DataRank of 0.498. It has been cited 8 times, with 8 citing works in its 1-hop citation network.

N/A

0.498DataRank · unranked

Scored on demand from live citation data

Open Access8 citations · 8 citing works in its network

Download PDF

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (12 grants)Impact (FWCI 0.70)OA: goldTopics (35)IDs (PubMed, PMC, OpenAlex)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

DataRank Breakdown

Base Score 66%Citation Network 34%

Base Score Contribution

0.330

From this paper's citation signal

Citation Network Contribution

0.169

From 5 citing papers with measurable signal

Learn more about DataRank methodology →

Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 8 citers.

Evaluating similitude and robustness of deep image denoising models via adversarial attack
Neurocomputing20263 citations21 referencesContributes 0.066
Towards Scalable and Robust Model Versioning
20244 citations39 referencesContributes 0.041
Adversarial Robust Aerial Image Recognition Based on Reactive-Proactive Defense Framework with Deep Ensembles
Remote Sensing202320 citations76 referencesContributes 0.040
A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
Applied Sciences20246 citations49 referencesContributes 0.040
Robust Subgraph Learning by Monitoring Early Training Representations
20251 citations61 referencesContributes 0.011

Authors (2)

Ge Wang ORCID,Christopher Wiedeman

Live enrichment

PubMed:35607626 PMC:PMC9122968 OpenAlex:https://openalex.org/W3198216469

National Institutes of Health

Grant: R01CA233888

National Institutes of Health

Grant: R01CA237267

National Institutes of Health

Grant: R01EB026646

National Institutes of Health

Grant: R01EB031102

National Institutes of Health

Grant: R01HL151561

National Institutes of Health

Grant: R21CA264772

National Institutes of Health

Grant: 1R01EB031102-01

Constrained Disentanglement (CODE) Network for CT Metal Artifact Reduction in Radiation Therapy

National Institutes of Health

Grant: 5R01CA233888-05

Development and Validation of Prognostic Radiomic Markers of Response and Recurrence for Patients with Colorectal Liver Metastases

National Institutes of Health

Grant: 5R01HL151561-03

Cardiac CT Deblooming

National Institutes of Health

Grant: 1R21CA264772-01

SPECT with a Compton Camera for Thyroid Cancer Imaging

National Institutes of Health

Grant: 5R01CA237267-02

Photon-counting X-ray and Optical Tomography for Preclinical Cancer Research

National Institutes of Health

Grant: 3R01EB026646-04S1

Life time imaging with pulsed x-ray based x-ray luminescence computed tomography

FWCI

0.70

Citation Percentile

0.7%

Citation Trend

2022

2023

2024

2025

2026

goldLicense: cc-by-nc-nd

journalhttp://www.cell.com/article/S2666389922000526/pdf

publisherhttp://www.cell.com/article/S2666389922000526/pdf

publisherhttps://api.elsevier.com/content/article/PII:S2666389922000526?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S2666389922000526?httpAccept=text/plain

journalhttps://doi.org/10.1016/j.patter.2022.100472

Fields of Study

Adversarial Robustness in Machine LearningAnomaly Detection Techniques and ApplicationsIntegrated Circuits and Semiconductor Failure Analysis02 engineering and technology01 natural sciences0202 electrical engineering, electronic engineering, information engineering0105 earth and related environmental sciences

Keywords

TransferabilityAdversarial systemComputer scienceAutoencoderArtificial intelligenceLinear subspaceFeature (linguistics)Deep learningInferenceMachine learningPattern recognition (psychology)MathematicsComputer visionDecorrelationRadiomicsAdversarial AttacksAttack TransferabilityFOS: Computer and information sciencesComputer Science - Machine LearningComputer Science - Cryptography and SecurityComputer Science - Artificial IntelligenceArticleMachine Learning (cs.LG)QA76.75-76.765Artificial Intelligence (cs.AI)DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problemComputer softwareCryptography and Security (cs.CR)

Sustainable Development Goals

SDG 0: Peace, Justice and strong institutions