On the public citation graph

Efficient Query-based Black-box Attack against Cross-modal Hashing Retrieval

https://doi.org/10.1145/3559758
0 signed · 76 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot
Microsoft COCO: Common Objects in Context
inferredcites →https://doi.org/10.1007/978-3-658-40442-0_9openalex-snapshot
Generative Adversarial Nets
inferredcites →https://doi.org/10.1007/s10707-020-00418-7openalex-snapshot
AP-GAN: Adversarial patch attack on content-based image retrieval systems
inferredcites →https://doi.org/10.1016/j.cosrev.2020.100336openalex-snapshot
Comparative analysis on cross-modal information retrieval: A review
inferredcites →https://doi.org/10.1109/cvpr.1994.323798openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.348openalex-snapshot
Deep Cross-Modal Hashing
inferredcites →https://doi.org/10.1109/cvpr.2019.00723openalex-snapshot
Feature Space Perturbations Yield More Transferable Adversarial Examples
inferredcites →https://doi.org/10.1109/secpri.2004.1301305openalex-snapshot
Proceedings 2004 IEEE Symposium on Security and Privacy
inferredcites →https://doi.org/10.1109/tcyb.2016.2606441openalex-snapshot
Semi-Paired Discrete Hashing: Learning Latent Hash Codes for Semi-Paired Cross-View Retrieval
inferredcites →https://doi.org/10.1109/tcyb.2018.2882908openalex-snapshot
Adversarial Examples for Hamming Space Search
inferredcites →https://doi.org/10.1109/tip.2018.2821921openalex-snapshot
Triplet-Based Deep Hashing Network for Cross-Modal Retrieval
inferredcites →https://doi.org/10.1109/tip.2019.2940693openalex-snapshot
Scalable Deep Hashing for Large-Scale Social Image Retrieval
inferredcites →https://doi.org/10.1109/tip.2020.2963957openalex-snapshot
Multi-Task Consistency-Preserving Adversarial Hashing for Cross-Modal Retrieval
inferredcites →https://doi.org/10.1109/tkde.2020.2987312openalex-snapshot
Deep Cross-Modal Hashing With Hashing Functions and Unified Hash Codes Jointly Learning
inferredcites →https://doi.org/10.1109/tmm.2020.2984081openalex-snapshot
Mask Cross-Modal Hashing Networks
inferredcites →https://doi.org/10.1109/tmm.2021.3053766openalex-snapshot
Aggregation-Based Graph Convolutional Hashing for Unsupervised Cross-Modal Retrieval
inferredcites →https://doi.org/10.1109/tmm.2021.3119868openalex-snapshot
Deep Co-Image-Label Hashing for Multi-Label Image Retrieval
inferredcites →https://doi.org/10.1109/tpami.2016.2572683openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.1109/tpami.2017.2699960openalex-snapshot
A Survey on Learning to Hash
inferredcites →https://doi.org/10.1145/1460096.1460104openalex-snapshot
The MIR flickr retrieval evaluation
inferredcites →https://doi.org/10.1145/1646396.1646452openalex-snapshot
NUS-WIDE
inferredcites →https://doi.org/10.1145/3372278openalex-snapshot
Proceedings of the 2020 International Conference on Multimedia Retrieval
inferredcites →https://doi.org/10.1145/3460426openalex-snapshot
Proceedings of the 2021 International Conference on Multimedia Retrieval
inferredcites →https://doi.org/10.24963/ijcai.2018/543openalex-snapshot
Generating Adversarial Examples with Adversarial Networks
inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot
AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale
inferredcites →https://doi.org/10.4230/oasics.ng-res.2022.1openalex-snapshot
Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper)
inferredcites →https://doi.org/10.4230/oasics.saia.2024.1openalex-snapshot
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot
Adam: A Method for Stochastic Optimization
inferredcites →https://doi.org/10.48550/arxiv.1706.06083openalex-snapshot
Towards Deep Learning Models Resistant to Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.2010.11742openalex-snapshot
Learning Black-Box Attackers with Transferable Priors and Query Feedback
inferredcites →https://doi.org/10.5244/c.28.6openalex-snapshot
Return of the Devil in the Details: Delving Deep into Convolutional Nets

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-72848-8_16openalex-snapshot
Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detectors
inferred← citeshttps://doi.org/10.1007/978-981-97-2112-2_6openalex-snapshot
Deep Collaborative Graph Hashing
inferred← citeshttps://doi.org/10.1007/s40747-025-01814-yopenalex-snapshot
Unsupervised random walk manifold contrastive hashing for multimedia retrieval
inferred← citeshttps://doi.org/10.1016/j.dsp.2026.106357openalex-snapshot
RetPur: Diffusion-based defense against black-box targeted attacks in hashing retrieval
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.128747openalex-snapshot
Deep semantic center-guided hashing for multi-label cross-modal retrieval
inferred← citeshttps://doi.org/10.1016/j.imavis.2025.105421openalex-snapshot
Correlation embedding semantic-enhanced hashing for multimedia retrieval
inferred← citeshttps://doi.org/10.1016/j.ins.2023.120064openalex-snapshot
LCEMH: Label Correlation Enhanced Multi-modal Hashing for efficient multi-modal retrieval
inferred← citeshttps://doi.org/10.1016/j.ipm.2025.104214openalex-snapshot
Diversified perturbation guided by optimal target code for cross-modal adversarial attack
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.111837openalex-snapshot
Supervised Contrastive Discrete Hashing for cross-modal retrieval
inferred← citeshttps://doi.org/10.1016/j.patcog.2023.109462openalex-snapshot
Deep collaborative graph hashing for discriminative image retrieval
inferred← citeshttps://doi.org/10.1016/j.patcog.2026.114124openalex-snapshot
MLCA: Multi-level Correlative Attacks against Deep Cross-Modal Hashing
inferred← citeshttps://doi.org/10.1109/access.2024.3371173openalex-snapshot
Deep Self-Supervised Hashing With Fine-Grained Similarity Mining for Cross-Modal Retrieval
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10889267openalex-snapshot
Unsupervised Hierarchical Dynamic Similarity Hashing for Multimedia Retrieval
inferred← citeshttps://doi.org/10.1109/icict57646.2023.10134138openalex-snapshot
A Novel Cross Validated Information Security Framework for Digital Information Systems
inferred← citeshttps://doi.org/10.1109/jproc.2024.3525147openalex-snapshot
Cross-Modal Retrieval: A Systematic Review of Methods and Future Directions
inferred← citeshttps://doi.org/10.1109/tce.2025.3559103openalex-snapshot
Untargeted Closed-Box Attack Against Healthcare Image Retrieval via Rank Manipulation
inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3263054openalex-snapshot
Targeted Adversarial Attack Against Deep Cross-Modal Hashing Retrieval
inferred← citeshttps://doi.org/10.1109/tcsvt.2023.3339489openalex-snapshot
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
inferred← citeshttps://doi.org/10.1109/tcsvt.2025.3529583openalex-snapshot
Revealing Security Flaws in Cross-Modal Retrieval Models Through Video Poisoning
inferred← citeshttps://doi.org/10.1109/tcsvt.2025.3546702openalex-snapshot
Toward a Universal, Transferable, and Robust Adversarial Perturbation Framework Against Deep Hashing-Based Facial Image Retrieval
inferred← citeshttps://doi.org/10.1109/tcsvt.2025.3604033openalex-snapshot
FPAD: Fuzzy-Prototype-Guided Adversarial Attack and Defense for Deep Cross-Modal Hashing
inferred← citeshttps://doi.org/10.1109/tcsvt.2025.3643601openalex-snapshot
Redundancy Mitigation: Toward Accurate and Efficient Image–Text Retrieval
inferred← citeshttps://doi.org/10.1109/tifs.2024.3518072openalex-snapshot
Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models via Diffusion Models
inferred← citeshttps://doi.org/10.1109/tifs.2025.3557737openalex-snapshot
Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks
inferred← citeshttps://doi.org/10.1109/tifs.2025.3574976openalex-snapshot
Modality-Specific Interactive Attack for Vision-Language Pre-Training Models
inferred← citeshttps://doi.org/10.1109/tip.2025.3607626openalex-snapshot
Prospective Layout-Guided Multi-Modal Online Hashing
inferred← citeshttps://doi.org/10.1109/tkde.2026.3665949openalex-snapshot
Deep Stochastic Spherical Hashing With Von Mises-Fisher Distributions for Cross-Modal Retrieval
inferred← citeshttps://doi.org/10.1109/tmm.2023.3345167openalex-snapshot
Iterative Adversarial Attack on Image-Guided Story Ending Generation
inferred← citeshttps://doi.org/10.1109/tmm.2024.3521697openalex-snapshot
Primary Code Guided Targeted Attack against Cross-modal Hashing Retrieval
inferred← citeshttps://doi.org/10.1109/tmm.2025.3543035openalex-snapshot
DCDL: Dual Causal Disentangled Learning for Zero-Shot Sketch-Based Image Retrieval
inferred← citeshttps://doi.org/10.1109/tmm.2025.3639961openalex-snapshot
Invisible Backdoor Attack With Siamese Tuning on Pre-Trained Vision-Language Models
inferred← citeshttps://doi.org/10.1109/tmm.2026.3673547openalex-snapshot
Revisiting the Adversarial Transferability: Toward a Perspective of Semantic Preservation
inferred← citeshttps://doi.org/10.1109/tpami.2025.3546334openalex-snapshot
Attack as Defense: Proactive Adversarial Multi-Modal Learning to Evade Retrieval
inferred← citeshttps://doi.org/10.1109/tpami.2026.3725315openalex-snapshot
Deep Supervised Adversarial Robust Hashing for Retrieval
inferred← citeshttps://doi.org/10.1145/3579995openalex-snapshot
Trustworthy Recommendation and Search: Introduction to the Special Issue - Part 1
inferred← citeshttps://doi.org/10.1145/3581783.3612364openalex-snapshot
Precise Target-Oriented Attack against Deep Hashing-based Retrieval
inferred← citeshttps://doi.org/10.1145/3624016openalex-snapshot
Complex Scenario Image Retrieval via Deep Similarity-aware Hashing
inferred← citeshttps://doi.org/10.1145/3650205openalex-snapshot
Invisible Black-Box Backdoor Attack against Deep Cross-Modal Hashing Retrieval
inferred← citeshttps://doi.org/10.1145/3721432openalex-snapshot
Deep Differential Lifelong Cross-modal Hashing for Stream Medical Data Retrieval
inferred← citeshttps://doi.org/10.1145/3805622.3810833openalex-snapshot
Spatial-Frequency Domain Complementary Learning for Robust Cross-Modal Hashing
inferred← citeshttps://doi.org/10.1145/3805712.3809611openalex-snapshot
Spectral-Adaptive Adversarial Hashing for Robust Image Retrieval
inferred← citeshttps://doi.org/10.1609/aaai.v38i6.28319openalex-snapshot
Once and for All: Universal Transferable Adversarial Perturbation against Deep Hashing-Based Facial Image Retrieval
inferred← citeshttps://doi.org/10.3390/app14093785openalex-snapshot
Exploring the Efficacy of Learning Techniques in Model Extraction Attacks on Image Classifiers: A Comparative Study
inferred← citeshttps://doi.org/10.36227/techrxiv.175037432.26316641/v1openalex-snapshot
A Survey on Image Data Perturbations: Challenges, Techniques, and Impacts
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.