CUDA: Convolution-Based Unlearnable Datasets
Open accessAbstract
Large-scale training of modern deep learning models heavily relies on publicly available data on the web. This potentially unauthorized usage of online data leads to concerns regarding data privacy. Recent works aim to make unlearnable data for deep learning models by adding small, specially designed noises to tackle this issue. However, these methods are vulnerable to adversarial training (AT) and/or are computationally heavy. In this work, we propose a novel, model-free, Convolution-based Unlearnable DAtaset (CUDA) generation technique. CUDA is generated using controlled class-wise convolutions with filters that are randomly generated via a private key. CUDA encourages the network to learn the relation between filters and labels rather than informative features for classifying the clean data. We develop some theoretical analysis demonstrating that CUDA can successfully poison Gaussian mixture data by reducing the clean data performance of the optimal Bayes classifier. We also empirically demonstrate the effectiveness of CUDA with various datasets (CIFAR-10, CIFAR-100, ImageNet-100, and Tiny-ImageNet), and architectures (ResNet-18, VGG-16, Wide ResNet-34-10, DenseNet-121, DeIT, EfficientNetV2-S, and MobileNetV2). Our experiments show that CUDA is robust to various data augmentations and training approaches such as smoothing, AT with different budgets, transfer learning, and fine-tuning. For instance, training a ResNet-18 on ImageNet-100 CUDA achieves only 8.96%, 40.08%, and 20.58% clean test accuracies with empirical risk minimization (ERM), L∞AT, and L2 AT, respectively. Here, ERM on the clean training data achieves a clean test accuracy of 80.66%. CUDA exhibits unlearnability effect with ERM even when only a fraction of the training dataset is perturbed. Furthermore, we also show that CUDA is robust to adaptive defenses designed specifically to break it.
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Neural network-based and zero-shot detectors rely on a deep net for their detection and can be vulnerable to adversarial and poisoning attacks.
“Additionally, neural network-based and zero-shot detectors rely on a deep net for their detection, and they can be vulnerable to adversarial and poisoning attacks (Goodfellow et al., 2014; Sadasivan et al., 2023; Kumar et al., 2022; Wang et al., 2022).”
From Can AI-Generated Text be Reliably Detected? (2023) · cites this paper as Sadasivan et al., 2023
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- Authors
- Sadasivan, V. S., Soltanolkotabi, M., & Feizi, S.
- Affiliations
- University of Maryland, College Park (US)
- University of Southern California (US)
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 3862-3871
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386076376
- Cited by
- 24
- References
- 60
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 10
- FWCI
- 4.25
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- NSF (1942230,HR001119S0026)
- ONR YIP (N00014-22-1-2271)
- NIST (60NANB20D134)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- data poisoning, adversarial training, data privacy, empirical risk minimization, adversarial defense, transfer learning, fine-tuning
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