# Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior

arXiv: https://arxiv.org/abs/2203.06560 · HTML: https://mlsanity.com/paper/2203.06560/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 52 references that are in the map.
- First posted on arXiv 13 Mar 2022.

## Builds on (most cited first, 30 of 36)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [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)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [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)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming](https://mlsanity.com/paper/1309.5549/) (2013)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [Black-box Adversarial Attacks with Limited Queries and Information](https://mlsanity.com/paper/1804.08598/) (2018)
- [Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models](https://mlsanity.com/paper/1712.04248/) (2017)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks](https://mlsanity.com/paper/1904.02884/) (2019)
- [Adversarial Risk and the Dangers of Evaluating Against Weak Attacks](https://mlsanity.com/paper/1802.05666/) (2018)
- [Bag of Tricks for Adversarial Training](https://mlsanity.com/paper/2010.00467/) (2020)
- [Optimal rates for zero-order convex optimization: the power of two function evaluations](https://mlsanity.com/paper/1312.2139/) (2013)
- [Efficient Decision-based Black-box Adversarial Attacks on Face Recognition](https://mlsanity.com/paper/1904.04433/) (2019)
- [Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors](https://mlsanity.com/paper/1807.07978/) (2018)

## Built on by (most cited first, 30 of 38)

- [On Evaluating Adversarial Robustness of Large Vision-Language Models](https://mlsanity.com/paper/2305.16934/) (2023)
- [How Robust is Google's Bard to Adversarial Image Attacks?](https://mlsanity.com/paper/2309.11751/) (2023)
- [Chain of Attack: On the Robustness of Vision-Language Models Against Transfer-Based Adversarial Attacks](https://mlsanity.com/paper/2411.15720/) (2024)
- [A Frustratingly Simple Yet Highly Effective Attack Baseline: Over 90% Success Rate Against the Strong Black-box Models of GPT-4.5/4o/o1](https://mlsanity.com/paper/2503.10635/) (2025)
- [Improving Fast Adversarial Training with Prior-Guided Knowledge](https://mlsanity.com/paper/2304.00202/) (2023)
- [Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models](https://mlsanity.com/paper/2210.02447/) (2022)
- [Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey](https://mlsanity.com/paper/2410.23687/) (2024)
- [Efficient Robustness Assessment via Adversarial Spatial-Temporal Focus on Videos](https://mlsanity.com/paper/2301.00896/) (2023)
- [Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective](https://mlsanity.com/paper/2302.09457/) (2023)
- [Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning](https://mlsanity.com/paper/2308.12636/) (2023)
- [Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World](https://mlsanity.com/paper/2304.10712/) (2023)
- [Efficient Black-box Adversarial Attacks via Bayesian Optimization Guided by a Function Prior](https://mlsanity.com/paper/2405.19098/) (2024)
- [Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical World](https://mlsanity.com/paper/2312.14217/) (2023)
- [Hard-label based Small Query Black-box Adversarial Attack](https://mlsanity.com/paper/2403.06014/) (2024)
- [V-Attack: Targeting Disentangled Value Features for Controllable Adversarial Attacks on LVLMs](https://mlsanity.com/paper/2511.20223/) (2025)
- [Scaling Laws for Black box Adversarial Attacks](https://mlsanity.com/paper/2411.16782/) (2024)
- [Adversarial Neon Beam: A Light-based Physical Attack to DNNs](https://mlsanity.com/paper/2204.00853/) (2022)
- [Box-Free Model Watermarks Are Prone to Black-Box Removal Attacks](https://mlsanity.com/paper/2405.09863/) (2024)
- [Pushing the Frontier of Black-Box LVLM Attacks via Fine-Grained Detail Targeting](https://mlsanity.com/paper/2602.17645/) (2026)
- [Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey](https://mlsanity.com/paper/2303.06302/) (2023)
- [A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking](https://mlsanity.com/paper/2306.12111/) (2023)
- [Decoder Gradient Shield: Provable and High-Fidelity Prevention of Gradient-Based Box-Free Watermark Removal](https://mlsanity.com/paper/2502.20924/) (2025)
- [Transcending Adversarial Perturbations: Manifold-Aided Adversarial Examples with Legitimate Semantics](https://mlsanity.com/paper/2402.03095/) (2024)
- [Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors](https://mlsanity.com/paper/2404.12120/) (2024)
- [INK: Inheritable Natural Backdoor Attack Against Model Distillation](https://mlsanity.com/paper/2304.10985/) (2023)
- [Attack Anything: Blind DNNs via Universal Background Adversarial Attack](https://mlsanity.com/paper/2409.00029/) (2024)
- [STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario](https://mlsanity.com/paper/2404.00362/) (2024)
- [Adversarial Color Projection: A Projector-based Physical Attack to DNNs](https://mlsanity.com/paper/2209.09652/) (2022)
- [Adversarial Catoptric Light: An Effective, Stealthy and Robust Physical-World Attack to DNNs](https://mlsanity.com/paper/2209.11739/) (2022)
- [Class-Conditional Neural Polarizer: A Lightweight and Effective Backdoor Defense by Purifying Poisoned Features](https://mlsanity.com/paper/2502.18520/) (2025)

## Abstract (arXiv)

Adversarial attacks have been extensively studied in recent years since they can identify the vulnerability of deep learning models before deployed. In this paper, we consider the black-box adversarial setting, where the adversary needs to craft adversarial examples without access to the gradients of a target model. Previous methods attempted to approximate the true gradient either by using the transfer gradient of a surrogate white-box model or based on the feedback of model queries. However, the existing methods inevitably suffer from low attack success rates or poor query efficiency since it is difficult to estimate the gradient in a high-dimensional input space with limited information. To address these problems and improve black-box attacks, we propose two prior-guided random gradient-free (PRGF) algorithms based on biased sampling and gradient averaging, respectively. Our methods can take the advantage of a transfer-based prior given by the gradient of a surrogate model and the query information simultaneously. Through theoretical analyses, the transfer-based prior is appropriately integrated with model queries by an optimal coefficient in each method. Extensive experiments demonstrate that, in comparison with the alternative state-of-the-arts, both of our methods require much fewer queries to attack black-box models with higher success rates.