mlsanityData to 7 Oct 2026

Adversarial Transformation Networks: Learning to Generate Adversarial Examples

arXiv 1703.09387 ↗First posted 28 Mar 2017cs.NE cs.AI cs.CVcited by 60

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Abstract (arXiv)

Multiple different approaches of generating adversarial examples have been proposed to attack deep neural networks. These approaches involve either directly computing gradients with respect to the image pixels, or directly solving an optimization on the image pixels. In this work, we present a fundamentally new method for generating adversarial examples that is fast to execute and provides exceptional diversity of output. We efficiently train feed-forward neural networks in a self-supervised manner to generate adversarial examples against a target network or set of networks. We call such a network an Adversarial Transformation Network (ATN). ATNs are trained to generate adversarial examples that minimally modify the classifier's outputs given the original input, while constraining the new classification to match an adversarial target class. We present methods to train ATNs and analyze their effectiveness targeting a variety of MNIST classifiers as well as the latest state-of-the-art ImageNet classifier Inception ResNet v2.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Mar 2017.

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Citing papers per year: 2021: 29; 2022: 10; 2023: 9; 2024: 3; 2025: 5; 2026: 4.

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  1. 2021GAN Inversion: A Surveycited by 366
  2. 2021Adversarial Example Detection for DNN Models: A Review and Experimental Comparisoncited by 51
  3. 2021RDA: Robust Domain Adaptation via Fourier Adversarial Attackingcited by 48
  4. 2022Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioningcited by 45
  5. 2022Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domainscited by 42
  6. 2022Self-Ensemble Adversarial Training for Improved Robustnesscited by 38
  7. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
  8. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  9. 2021Defending Against Image Corruptions Through Adversarial Augmentationscited by 28
  10. 2021BASAR:Black-box Attack on Skeletal Action Recognitioncited by 25
  11. 2021Parameter identifiability of a deep feedforward ReLU neural networkcited by 24
  12. 2021Unsupervised Robust Domain Adaptation without Source Datacited by 19
38 more
  1. 2021Generative Dynamic Patch Attackcited by 19
  2. 2021Security and Privacy for Artificial Intelligence: Opportunities and Challengescited by 16
  3. 2021Generative Adversarial Networks: A Survey Towards Private and Secure Applicationscited by 15
  4. 2021Generating Adversarial Examples with Graph Neural Networkscited by 12
  5. 2021Black-box adversarial attacks using Evolution Strategiescited by 11
  6. 2025MMT-ARD: Multimodal Multi-Teacher Adversarial Distillation for Robust Vision-Language Modelscited by 11
  7. 2021Stabilized Medical Image Attackscited by 10
  8. 2023SketchXAI: A First Look at Explainability for Human Sketchescited by 10
  9. 2021Transferable Sparse Adversarial Attackcited by 9
  10. 2021Prototype-supervised Adversarial Network for Targeted Attack of Deep Hashingcited by 8
  11. 2021Selection of Source Images Heavily Influences the Effectiveness of Adversarial Attackscited by 6
  12. 2021Semantic Perturbations with Normalizing Flows for Improved Generalizationcited by 6
  13. 2023SoK: Pitfalls in Evaluating Black-Box Attackscited by 6
  14. 2021Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropoutcited by 5
  15. 2023A Review of Adversarial Attacks in Computer Visioncited by 5
  16. 2022Data-Efficient Augmentation for Training Neural Networkscited by 4
  17. 2025AIM: Additional Image Guided Generation of Transferable Adversarial Attackscited by 4
  18. 2021Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustnesscited by 3
  19. 2021ZeBRA: Precisely Destroying Neural Networks with Zero-Data Based Repeated Bit Flip Attackcited by 3
  20. 2021Robust Training Using Natural Transformationcited by 2
  21. 2022Surround-View Cameras based Holistic Visual Perception for Automated Drivingcited by 2
  22. 2022A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection Systemcited by 2
  23. 2024Towards Non-Adversarial Algorithmic Recoursecited by 2
  24. 2024Query-Efficient Hard-Label Black-Box Attack against Vision Transformerscited by 2
  25. 2021PAT: Pseudo-Adversarial Training For Detecting Adversarial Videoscited by 1
  26. 2022Attacks as Defenses: Designing Robust Audio CAPTCHAs Using Attacks on Automatic Speech Recognition Systemscited by 1
  27. 2022Localized Shortcut Removalcited by 1
  28. 2023Do we need entire training data for adversarial training?cited by 1
  29. 2024GRACE: Graph-Regularized Attentive Convolutional Entanglement with Laplacian Smoothing for Robust DeepFake Video Detectioncited by 1
  30. 2025How to Backdoor Image Knowledge Distillationcited by 1
  31. 2021MixDefense: A Defense-in-Depth Framework for Adversarial Example Detection Based on Statistical and Semantic Analysiscited by 0
  32. 2021Benign Adversarial Attack: Tricking Models for Goodnesscited by 0
  33. 2021BOSS: Bidirectional One-Shot Synthesis of Adversarial Examplescited by 0
  34. 2021Towards Efficiently Evaluating the Robustness of Deep Neural Networks in IoT Systems: A GAN-based Methodcited by 0
  35. 2021Using a GAN to Generate Adversarial Examples to Facial Image Recognitioncited by 0
  36. 2021Invertible Image Dataset Protectioncited by 0
  37. 2022Towards Robust Stacked Capsule Autoencoder with Hybrid Adversarial Trainingcited by 0
  38. 2022Securing AI-based Healthcare Systems using Blockchain Technology: A State-of-the-Art Systematic Literature Review and Future Research Directionscited by 0

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