Defense Against the Dark Arts: An overview of adversarial example security research and future research directions
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Abstract (arXiv)
This article presents a summary of a keynote lecture at the Deep Learning Security workshop at IEEE Security and Privacy 2018. This lecture summarizes the state of the art in defenses against adversarial examples and provides recommendations for future research directions on this topic.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2018.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 2; 2024: 1; 2025: 3.
11 papers.
- 2021Adversarial Token Attacks on Vision Transformerscited by 11
- 2023PubDef: Defending Against Transfer Attacks From Public Modelscited by 7
- 2022Careful What You Wish For: on the Extraction of Adversarially Trained Modelscited by 6
- 2021Towards Making Deep Learning-based Vulnerability Detectors Robustcited by 4
- 2023Fast Certification of Vision-Language Models Using Incremental Randomized Smoothingcited by 4
- 2022Smooth-Reduce: Leveraging Patches for Improved Certified Robustnesscited by 1
- 2025IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Contextcited by 1
- 2021Robusta: Robust AutoML for Feature Selection via Reinforcement Learningcited by 0
- 2024Evaluating Adversarial Robustness in the Spatial Frequency Domaincited by 0
- 2025How can we trust opaque systems? Criteria for robust explanations in XAIcited by 0
- 2025DivQAT: Enhancing Robustness of Quantized Convolutional Neural Networks against Model Extraction Attackscited by 0
- 2025DivQAT: Enhancing Robustness of Quantized Convolutional Neural Networks against Model Extraction Attacks30 Dec 2025
- 2025IndiCASA: A Dataset and Bias Evaluation Framework in LLMs Using Contrastive Embedding Similarity in the Indian Context3 Oct 2025
- 2025How can we trust opaque systems? Criteria for robust explanations in XAI18 Aug 2025
- 2024Evaluating Adversarial Robustness in the Spatial Frequency Domain10 May 2024
- 2023Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing15 Nov 2023
- 2023PubDef: Defending Against Transfer Attacks From Public Models26 Oct 2023
- 2022Careful What You Wish For: on the Extraction of Adversarially Trained Models21 Jul 2022
- 2022Smooth-Reduce: Leveraging Patches for Improved Certified Robustness12 May 2022
- 2021Adversarial Token Attacks on Vision Transformers8 Oct 2021
- 2021Towards Making Deep Learning-based Vulnerability Detectors Robust2 Aug 2021
- 2021Robusta: Robust AutoML for Feature Selection via Reinforcement Learning15 Jan 2021