On the public citation graph

Learning Ordered Top-k Adversarial Attacks via Adversarial Distillation

https://doi.org/10.1109/cvprw50498.2020.00396
0 signed · 55 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/0893-6080(89)90020-8openalex-snapshot
Multilayer feedforward networks are universal approximators
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/access.2018.2807385openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferredcites →https://doi.org/10.1109/cvpr.2015.7298640openalex-snapshot
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot
Boosting Adversarial Attacks with Momentum
inferredcites →https://doi.org/10.1109/cvpr.2019.00059openalex-snapshot
Feature Denoising for Improving Adversarial Robustness
inferredcites →https://doi.org/10.1109/cvpr.2019.00284openalex-snapshot
Improving Transferability of Adversarial Examples With Input Diversity
inferredcites →https://doi.org/10.1109/cvpr.2019.00444openalex-snapshot
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
inferredcites →https://doi.org/10.1109/cvpr.2019.00445openalex-snapshot
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
inferredcites →https://doi.org/10.1109/cvpr.2019.00638openalex-snapshot
AOGNets: Compositional Grammatical Architectures for Deep Learning
inferredcites →https://doi.org/10.1109/iccv.2017.153openalex-snapshot
Adversarial Examples for Semantic Segmentation and Object Detection
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/spw.2018.00014openalex-snapshot
Adversarial Examples for Generative Models
inferredcites →https://doi.org/10.1109/tevc.2019.2890858openalex-snapshot
One Pixel Attack for Fooling Deep Neural Networks
inferredcites →https://doi.org/10.1145/1150402.1150464openalex-snapshot
Model compression
inferredcites →https://doi.org/10.1145/2976749.2978392openalex-snapshot
Accessorize to a Crime
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3128572.3140448openalex-snapshot
ZOO
inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.1609/aaai.v32i1.11302openalex-snapshot
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
inferredcites →https://doi.org/10.18653/v1/p18-2006openalex-snapshot
HotFlip: White-Box Adversarial Examples for NLP
inferredcites →https://doi.org/10.24963/ijcai.2017/525openalex-snapshot
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
inferredcites →https://doi.org/10.24963/ijcai.2021/694openalex-snapshot
Adversarial Examples in Physical World
inferredcites →https://doi.org/10.3115/v1/d14-1162openalex-snapshot
Glove: Global Vectors for Word Representation
inferredcites →https://doi.org/10.4230/lipics.itcs.2019.16openalex-snapshot
Adversarial Logit Pairing
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.1312.6199openalex-snapshot
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-snapshot
Distilling the Knowledge in a Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1611.02770openalex-snapshot
Delving into Transferable Adversarial Examples and Black-box Attacks
inferredcites →https://doi.org/10.48550/arxiv.1705.05264openalex-snapshot
Extending Defensive Distillation
inferredcites →https://doi.org/10.48550/arxiv.1707.07397openalex-snapshot
Synthesizing Robust Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1808.01664openalex-snapshot
Structured Adversarial Attack: Towards General Implementation and Better Interpretability
inferredcites →https://doi.org/10.48550/arxiv.1903.10346openalex-snapshot
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4350314openalex-extracted
inferred← citeshttps://doi.org/10.1007/978-3-031-33458-0_2openalex-snapshot
A Robust NFT Assisted Knowledge Distillation Framework for Edge Computing
inferred← citeshttps://doi.org/10.1016/j.imavis.2023.104722openalex-snapshot
Adversarial anchor-guided feature refinement for adversarial defense
inferred← citeshttps://doi.org/10.1016/j.jisa.2023.103483openalex-snapshot
Hybrid KD-NFT: A multi-layered NFT assisted robust Knowledge Distillation framework for Internet of Things
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112621openalex-snapshot
Similarity-driven adversarial testing of neural networks
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.127754openalex-snapshot
Dynamic loss yielding more transferable targeted adversarial examples
inferred← citeshttps://doi.org/10.1109/cvpr52688.2022.01466openalex-snapshot
Investigating Top-k White-Box and Transferable Black-box Attack
inferred← citeshttps://doi.org/10.1109/iccv48922.2021.00755openalex-snapshot
TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning
inferred← citeshttps://doi.org/10.1109/tii.2024.3383517openalex-snapshot
Sparse Adversarial Video Attack Based on Dual-Branch Neural Network on Industrial Artificial Intelligence of Things
inferred← citeshttps://doi.org/10.1109/tpami.2023.3296062openalex-snapshot
Rank-based Decomposable Losses in Machine Learning: A Survey
inferred← citeshttps://doi.org/10.1145/3581783.3611846openalex-snapshot
When Measures are Unreliable: Imperceptible Adversarial Perturbations toward Top-k Multi-Label Learning
inferred← citeshttps://doi.org/10.1145/3636534.3649367openalex-snapshot
Practical Adversarial Attack on WiFi Sensing Through Unnoticeable Communication Packet Perturbation
inferred← citeshttps://doi.org/10.24963/ijcai.2023/508openalex-snapshot
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
inferred← citeshttps://doi.org/10.3390/jimaging10110264openalex-snapshot
The Adversarial Robust and Generalizable Stereo Matching for Infrared Binocular Based on Deep Learning
inferred← citeshttps://doi.org/10.48550/arxiv.2108.00146openalex-snapshot
T$_k$ML-AP: Adversarial Attacks to Top-$k$ Multi-Label Learning
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.