# CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attacks

arXiv: https://arxiv.org/abs/2407.10179 · HTML: https://mlsanity.com/paper/2407.10179/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 55 of its 71 references that are in the map.
- First posted on arXiv 14 Jul 2024.

## Builds on (most cited first, 30 of 55)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)
- [Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks](https://mlsanity.com/paper/1908.06281/) (2019)
- [Prompting Visual-Language Models for Efficient Video Understanding](https://mlsanity.com/paper/2112.04478/) (2021)
- [Generating Adversarial Examples with Adversarial Networks](https://mlsanity.com/paper/1801.02610/) (2018)
- [Enhancing the Transferability of Adversarial Attacks through Variance Tuning](https://mlsanity.com/paper/2103.15571/) (2021)
- [A study of the effect of JPG compression on adversarial images](https://mlsanity.com/paper/1608.00853/) (2016)
- [Generative Adversarial Perturbations](https://mlsanity.com/paper/1712.02328/) (2017)
- [Cross-Domain Transferability of Adversarial Perturbations](https://mlsanity.com/paper/1905.11736/) (2019)
- [On Success and Simplicity: A Second Look at Transferable Targeted Attacks](https://mlsanity.com/paper/2012.11207/) (2020)
- [GALIP: Generative Adversarial CLIPs for Text-to-Image Synthesis](https://mlsanity.com/paper/2301.12959/) (2023)
- [Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability](https://mlsanity.com/paper/2111.10752/) (2021)

## Built on by (most cited first, 19 of 19)

- [Grounding Language with Vision: A Conditional Mutual Information Calibrated Decoding Strategy for Reducing Hallucinations in LVLMs](https://mlsanity.com/paper/2505.19678/) (2025)
- [Revisiting Backdoor Attacks on LLMs: A Stealthy and Practical Poisoning Framework via Harmless Inputs](https://mlsanity.com/paper/2505.17601/) (2025)
- [Retrievals Can Be Detrimental: Unveiling the Backdoor Vulnerability of Retrieval-Augmented Diffusion Models](https://mlsanity.com/paper/2501.13340/) (2025)
- [Protecting Your Video Content: Disrupting Automated Video-based LLM Annotations](https://mlsanity.com/paper/2503.21824/) (2025)
- [Editable-DeepSC: Reliable Cross-Modal Semantic Communications for Facial Editing](https://mlsanity.com/paper/2411.15702/) (2024)
- [Superplatforms Have to Attack AI Agents](https://mlsanity.com/paper/2505.17861/) (2025)
- [Turing Test on Screen: A Benchmark for Mobile GUI Agent Humanization](https://mlsanity.com/paper/2604.09574/) (2026)
- [TTP: Test-Time Padding for Adversarial Detection and Robust Adaptation on Vision-Language Models](https://mlsanity.com/paper/2512.16523/) (2025)
- [Enhancing Gradient Inversion Attacks in Federated Learning via Hierarchical Feature Optimization](https://mlsanity.com/paper/2604.00955/) (2026)
- [Bypassing Copyright Protection in Diffusion-based Customization via Two-Stage Latent Feature Optimization](https://mlsanity.com/paper/2606.09909/) (2026)
- [Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via In-the-wild Cascading Flow Optimization](https://mlsanity.com/paper/2502.02096/) (2025)
- [Neural Antidote: Class-Wise Prompt Tuning for Purifying Backdoors in CLIP](https://mlsanity.com/paper/2502.19269/) (2025)
- [Rethinking Target Label Conditioning in Adversarial Attacks: A 2D Tensor-Guided Generative Approach](https://mlsanity.com/paper/2504.14137/) (2025)
- [Towards Cross-Domain Multi-Targeted Adversarial Attacks](https://mlsanity.com/paper/2505.20782/) (2025)
- [Improving Black-Box Generative Attacks via Generator Semantic Consistency](https://mlsanity.com/paper/2506.18248/) (2025)
- [Revisiting the Privacy Risks of Split Inference: A GAN-Based Data Reconstruction Attack via Progressive Feature Optimization](https://mlsanity.com/paper/2508.20613/) (2025)
- [Improving Sustainability of Adversarial Examples in Class-Incremental Learning](https://mlsanity.com/paper/2511.09088/) (2025)
- [DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models](https://mlsanity.com/paper/2605.18868/) (2026)
- [T-VSS: Test-Time Visual Subspace Steering for Adversarial Robustness of Vision-Language Models](https://mlsanity.com/paper/2606.23132/) (2026)

## Abstract (arXiv)

Transferable targeted adversarial attacks aim to mislead models into outputting adversary-specified predictions in black-box scenarios. Recent studies have introduced textitsingle-target generative attacks that train a generator for each target class to generate highly transferable perturbations, resulting in substantial computational overhead when handling multiple classes. textitMulti-target attacks address this by training only one class-conditional generator for multiple classes. However, the generator simply uses class labels as conditions, failing to leverage the rich semantic information of the target class. To this end, we design a CLIP-guided Generative Network with Cross-attention modules (CGNC) to enhance multi-target attacks by incorporating textual knowledge of CLIP into the generator. Extensive experiments demonstrate that CGNC yields significant improvements over previous multi-target generative attacks, e.g., a 21.46% improvement in success rate from ResNet-152 to DenseNet-121. Moreover, we propose a masked fine-tuning mechanism to further strengthen our method in attacking a single class, which surpasses existing single-target methods.