On the public citation graph

Evaluating Impact of Image Transformations on Adversarial Examples

https://doi.org/10.1109/access.2024.3487479
0 signed · 36 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1002/int.22258openalex-snapshot
Defense against adversarial attacks by low‐level image transformations
inferredcites →https://doi.org/10.1007/11816157_147openalex-snapshot
Fast Affine Transform for Real-Time Machine Vision Applications
inferredcites →https://doi.org/10.1007/s11263-019-01213-0openalex-snapshot
Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks
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/access.2018.2890127openalex-snapshot
Multiple Feature Reweight DenseNet for Image Classification
inferredcites →https://doi.org/10.1109/com-it-con54601.2022.9850531openalex-snapshot
Facial Privacy Preservation using FGSM and Universal Perturbation attacks
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot
Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2017.243openalex-snapshot
Densely Connected Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00357openalex-snapshot
Defense Against Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00196openalex-snapshot
Enhancing the Transferability of Adversarial Attacks through Variance Tuning
inferredcites →https://doi.org/10.1109/pic50277.2020.9350850openalex-snapshot
An Improved Adversarial Example Generating Method With Optimized Spatial Transfrom
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/tdsc.2018.2874243openalex-snapshot
Detecting Adversarial Image Examples in Deep Neural Networks with Adaptive Noise Reduction
inferredcites →https://doi.org/10.1109/tifs.2020.3003571openalex-snapshot
Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
inferredcites →https://doi.org/10.1109/tip.2008.919949openalex-snapshot
Adaptive Bilateral Filter for Sharpness Enhancement and Noise Removal
inferredcites →https://doi.org/10.1109/tsmc.1973.4309317openalex-snapshot
Modeling Error Compensation in Nonlinear Estimation Problems
inferredcites →https://doi.org/10.1109/wacv56688.2023.00141openalex-snapshot
Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently
inferredcites →https://doi.org/10.1117/12.465289openalex-snapshot
Method for the estimation and recovering from general affine transforms in digital watermarking applications
inferredcites →https://doi.org/10.1201/9781351251389-8openalex-snapshot
Adversarial Examples in the Physical World
inferredcites →https://doi.org/10.3390/app9050909openalex-snapshot
Review of Artificial Intelligence Adversarial Attack and Defense Technologies
inferredcites →https://doi.org/10.48550/arxiv.1705.07204openalex-snapshot
Ensemble Adversarial Training: Attacks and Defenses
inferredcites →https://doi.org/10.48550/arxiv.2404.04245openalex-snapshot
Evaluating Adversarial Robustness: A Comparison Of FGSM, Carlini-Wagner Attacks, And The Role of Distillation as Defense Mechanism
inferredcites →https://doi.org/10.5772/intechopen.112442openalex-snapshot
Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and Their Impact

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.5137026openalex-extracted
inferred← citeshttps://doi.org/10.1007/978-3-031-97709-1_2openalex-snapshot
Defensive Classifier Methods: Feature Extraction and Behavior Analysis for Prediction
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.126044openalex-snapshot
Detection and prevention of evasion attacks on machine learning models
inferred← citeshttps://doi.org/10.1049/ipr2.70432openalex-snapshot
A Unified Adversarial Defence Framework: Diffusion‐Driven Purification with Adaptive Timestep Selection and Detection via Reconstruction Consistency Features
inferred← citeshttps://doi.org/10.1109/fmlds67896.2025.00018openalex-snapshot
On Robustness of Vision-Language Models Under Adversarial Perturbations
inferred← citeshttps://doi.org/10.1109/icpcsn68523.2026.11543651openalex-snapshot
An MRI-based Alzheimer's Disease Classification Model with Swin Transformer
inferred← citeshttps://doi.org/10.15622/rcai.2025.073openalex-snapshot
ИССЛЕДОВАНИЕ ВОЗМОЖНОСТЕЙ КОМБИНИРОВАНИЯ МЕТОДОВ ЗАЩИТЫ СИСТЕМ РАСПОЗНАВАНИЯ ИЗОБРАЖЕНИЙ ОТ ГРАДИЕНТНЫХ СОСТЯЗАТЕЛЬНЫХ АТАК
inferred← citeshttps://doi.org/10.2139/ssrn.5457015openalex-snapshot
Balancing Image Quality and Attack Effectiveness in Multi-Objective Adversarial Image Generation
inferred← citeshttps://doi.org/10.35377/saucis...1721392openalex-snapshot
A Defensive Distillation Approach Towards Adversarial Attacks Based on Gaussian Blurring
inferred← citeshttps://doi.org/10.9734/jerr/2025/v27i21413openalex-snapshot
Adversarial Threats to AI-Driven Systems: Exploring the Attack Surface of Machine Learning Models and Countermeasures
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.