mlsanityData to 14 Oct 2026

AdvGAN++ : Harnessing latent layers for adversary generation

arXiv 1908.00706 ↗First posted 2 Aug 2019cs.CV cs.LGcited by 20

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Abstract (arXiv)

Adversarial examples are fabricated examples, indistinguishable from the original image that mislead neural networks and drastically lower their performance. Recently proposed AdvGAN, a GAN based approach, takes input image as a prior for generating adversaries to target a model. In this work, we show how latent features can serve as better priors than input images for adversary generation by proposing AdvGAN++, a version of AdvGAN that achieves higher attack rates than AdvGAN and at the same time generates perceptually realistic images on MNIST and CIFAR-10 datasets.

Key facts. Cited by 20 papers in the map (data through 14 Oct 2026). First posted on arXiv 2 Aug 2019.

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Citing papers per year: 2021: 3; 2022: 4; 2023: 7; 2024: 4; 2025: 2.

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  1. 2021TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systemscited by 36
  2. 2023Generalizable Black-Box Adversarial Attack with Meta Learningcited by 29
  3. 2021LAFEAT: Piercing Through Adversarial Defenses with Latent Featurescited by 24
  4. 2023Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challengescited by 17
  5. 2022Adversarial Camouflage for Node Injection Attack on Graphscited by 11
  6. 2022Scale-free and Task-agnostic Attack: Generating Photo-realistic Adversarial Patterns with Patch Quilting Generatorcited by 5
  7. 2025Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systemscited by 4
  8. 2022On the Robustness of Deep Clustering Models: Adversarial Attacks and Defensescited by 2
  9. 2023OMG-ATTACK: Self-Supervised On-Manifold Generation of Transferable Evasion Attackscited by 2
  10. 2023Semantic Adversarial Attacks on Face Recognition through Significant Attributescited by 1
  11. 2023Latent Magic: An Investigation into Adversarial Examples Crafted in the Semantic Latent Spacecited by 1
  12. 2024Deep generative models as an adversarial attack strategy for tabular machine learningcited by 1
8 more
  1. 2021Using a GAN to Generate Adversarial Examples to Facial Image Recognitioncited by 0
  2. 2022Improving Robustness of Convolutional Neural Networks Using Element-Wise Activation Scalingcited by 0
  3. 2023Immune Defense: A Novel Adversarial Defense Mechanism for Preventing the Generation of Adversarial Examplescited by 0
  4. 2023A Survey of Adversarial CAPTCHAs on its History, Classification and Generationcited by 0
  5. 2024Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!cited by 0
  6. 2024GraphCroc: Cross-Correlation Autoencoder for Graph Structural Reconstructioncited by 0
  7. 2024NODE-AdvGAN: Improving the transferability and perceptual similarity of adversarial examples by dynamic-system-driven adversarial generative modelcited by 0
  8. 2025PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGANcited by 0