INTRODUCTIONRecent advancements of deep neural networks (DNNs) [21,37,15] heavily rely on the abundant availability of data resources [5,33,19]. However, the unauthorized collection of large-scale data through web scraping for model training has raised concerns regarding data security and privacy. In response to these concerns, a new paradigm of practical and effective data protection methods has emerged, known as availability poisoning attacks (APA) [40,45,9,17,43,10,32,14,36,8,44,14,32], or unlearnable example attacks. These poisoning methods inject small perturbations into images that are typically imperceptible to humans, in order to hinder the model's ability to learn the original features of the images. Recently, the field of deep learning has witnessed advancements in defense strategies [23,30,7,17] that hold the potential to challenge APAs, thereby undermining their claimed effectiveness and robustness. These defenses reveal the glaring inadequacy of existing APAs in safeguarding individual privacy in images. Consequently, we anticipate an impending arms race between attack and defense strategies in the near future.However, evaluating the performance of these new methods across diverse model architectures and datasets poses a significant challenge due to variations in experimental settings of recent literatures. In addition, researchers face the daunting task of staying abreast of the latest methods and assessing the effectiveness of various competing attack-defense combinations. This could greatly hamper the development and empirical exploration of novel attack and defense strategies.To tackle this challenge, we propose the APBench, a benchmark specifically designed for availability poisoning attacks and defenses. It involves implementing poisoning attack and defense mechanisms under standardized perturbations and training hyperparameters, in order to ensure fair and reproducible comparative evaluations. APBench comprises a range of availability poisoning attacks and defense algorithms, and commonly-used data augmentation policies. This comprehensive suite allows us to evaluate the effectiveness of the poisoning attacks thoroughly.Our contributions can be summarized as follows: 1 
Link to follow• An open source benchmark for state-of-the-art availability poisoning attacks and defenses, including 9 supervised and 2 unsupervised poisoning attack methods, 9 defense strategies and 4 common data augmentation methods. • We conduct a comprehensive evaluation competing pairs of poisoning attacks and defenses. • We conducted experiments across 4 publicly available datasets, and also extensively examined scenarios of partial poisoning, increased perturbations, the transferability of attacks to 4 CNN and 2 ViT models under various defenses, and unsupervised learning. We provide visual evaluation tools such as t-SNE, Shapley value map and Grad-CAM to qualitatively analyze the impact of poisoning attacks.The aim of APBench is to serve as a catalyst for facilitating and promoting future advancements in both availability poisoning attack and defense methods. By providing a platform for evaluation and comparison, we aspire to pave the way for the development of future availability poisoning attacks that can effectively preserve utility and protect privacy.
RELATED WORK
AVAILABILITY POISONING ATTACKSAvailability poisoning attacks (APAs) belong to a category of data poisoning attacks [12] that adds a small perturbation to images, that is often imperceptible to humans. However, the objective contrasts with that of traditional data poisoning. The purpose of these perturbations is to protect individual privacy from deep learning algorithms, preventing DNNs from effectively learning the features present in the images. The attacker's goal is to thus render their data unlearnable with perturbations, hindering the unauthorized trainer from utilizing the data to learn models that can generalize effectively to the original data distribution. The intent of APAs is therefore benign rather than malicious as generally assumed of data poisoning attacks. We typically assume that the attacker publishes (a subset of) the images, which get curated and accurately labeled by the defender to train on them without consent from the attacker. max δ E (xi,yi)∼Dtest [L(f θ (δ) (x i ), y i )], s.t. θ (δ) = argmin θ E (xi,yi)∼Dpoi(δ) L(f θ (x i ), y i ),(1)where L denotes the loss function, usually the softmax cross-entropy loss. In order to limit the impact on the original utility of images, the perturbation δ i is generally constrained within a small -ball of p distance.To enforce a small perturbation budget, recent methods typically constrain their perturbations within a small p -ball of radius, where typically p ∈ {0, 2, ∞}. DeepConfuse (DC) [8] proposes to use autoencoders to generate training-phase adversarial perturbations. Neural tangent generalization attacks (NTGA) [45] approximates the target model as a Gaussian process [18] using the generalized neural tangent kernel, and solves a bi-level optimization for perturbations. Error-minimizing attacks (EM) [17] minimizes the training error of the perturbed images relative to their original labels on the target model, creating shortcuts for the data to become "unlearnable" by the target model. Building upon EM, robust error-minimizing attacks (REM) [10] use adversarially trained models to generate perturbations in order to counter defense with adversarial training. Hypocritical [40] also generates error-minimizing perturbations similar to EM, but instead uses a pretrained surrogate model. Targeted adversarial poisoning (TAP) [9], inspired by [26], found adversarial examples could be used for availability poisoning. In contrast to the above approaches, indiscriminate poisoning (UCL) [14] and transferable unlearnable examples (TUE) [32] instead consider availability poisoning for unsupervised learning. On the other hand, 2 and 0 perturbation-based poisoning methods do not require a surrogate model. They achieve poisoning by searching for certain triggering patterns to create shortcuts in the network. Besides the above ∞ -bounded methods, Linear-separable poisoning (LSP) [44] and Autoregressive Poisoning (AR) [36] both prescribe perturbations within an 2 perturbation budget. Specifically, LSP generates randomly initialized linearly separable color block perturbations, while AR fills the starting rows and columns of each channel with Gaussian noise and uses an autoregressive process to fill the remaining pixels, generating random noise perturbations. One Pixel Shortcut [43] (OPS), as an 0 -bounded poisoning method, perturbs only a single pixel in the training image to achieve strong poisoning in terms of usability. Figure 1 provides visual examples of these attacks. 
AVAILABILITY POISONING DEFENSESThe goal of the defender is to successfully train a model with good generalization abilities (e.g., test accuracies on natural unseen images) on protected data. Generally, the defender can control the training algorithm, and only have access to a training data set with data poisoned either partially or fully. The objective of the defender is thus to find a novel training algorithm g(D poi ) that trains models to generalize well to the original data distribution:min g E (xi,yi)∼Dtest [L(f θ (x i ), y i )], s.t. θ = g(D poi ).(2)Notably, if the method employs the standard training loss but performs novel image transformations h, then g can be further specialized as follows:g(D poi ) = argmin θ E (xi,yi)∼Dpoi(δ) L(f θ (h(x i )), y i ).(3)Currently, defense methods against perturbative availability poisoning can be mainly classified into two categories: preprocessing and training-phase defenses. Data preprocessing methods preprocess the training images to eliminate the poisoning perturbations prior to training. Image shortcuts squeezing (ISS) [23] consists of simple countermeasures based on image compression, including grayscale transformation, JPEG compression, or bit-depth reduction (BDR) to perform poison removal. Recently, AVATAR [7] leverages the method proposed in DiffPure [28] to employ diffusion models to disrupt deliberate perturbations while preserving semantics in the training images. On the other hand, training-phase defense algorithms apply specific modifications to the training phase to defense against availability attacks. Adversarial training has long been considered the most effective defense mechanism [17,10] against such attacks. Recent report [35] finds that peak accuracy can be reached early in the training of availability poisons, and thus early stopping can be an effective mean of training-phase defense. Adversarial augmentations [30] sample multiple augmentations on one image, and train models on the maximum loss of all augmented images to prevent learning from poisoning shortcuts. For referential baselines, APBench also includes commonly used data augmentation techniques such as Gaussian blur, random crop and flip (standard training), CutOut [6], CutMix [46], and MixUp [47], and show their (limited) effect in mitigating availability poisons.
RELATED BENCHMARKSAvailability poisoning is closely connected to the domains of adversarial and backdoor attack and defense algorithms. Adversarial attacks primarily aim to deceive models with adversarial perturbations during inference to induce misclassifications. There are several libraries and benchmarks available for evaluating adversarial attack and defense techniques, such as Foolbox [31], AdvBox [13],and RobustBench [4].Backdoor or data poisoning [3] attacks focus on injecting backdoor triggers into the training algorithm or data respectively, causing trained models to misclassify images containing these triggers while maintaining or minimally impacting clean accuracy. In contrast to APAs, such attacks introduce hidden behaviors into the model that can be triggered by specific inputs, often for malicious purposes. Benchmark libraries specifically designed for backdoor attacks and defenses include Tro-janZoo [29], Backdoorbench [42], and Backdoorbox [22]. Moreover, [38,11] introduce benchmarks and frameworks for data poisoning attacks.However, there is currently a lack and an urgent need of a dedicated and comprehensive benchmark that standardizes and evaluates availability poisoning attack and defense strategies. To the best of our knowledge, APBench is the first benchmark that fulfills this purpose. It offers an extensive library of recent attacks and defenses, explores various perspectives, including the impact of poisoning rates and model architectures, as well as attack transferability. We hope that APBench can make significant contributions to the community and foster the development of future availability attacks for effective privacy protection.  We built an extensible codebase as the foundation of APBench. In the attack module, we provide a total of 9 availability poisoning attacks of 3 different perturbation types ( p ) for supervised learning, and 2 attacks for unsupervised learning. For each availability poisoning attack method, we can generate their respective poisoned datasets. This module also allows us to further expand to different perturbations budgets, poisoning ratios, and easily extend to future poisoning methods. Using the poisoned datasets generated by the attack module, we can evaluate defenses through the defense module. The goal of this module is to ensure that models trained on unlearnable datasets can still generalize well on clean data. The defense module primarily achieves poisoning mitigation through data preprocessing or training-phase defenses. Finally, the evaluation module computes the accuracy S HYPO [40] S EM [17] S REM [10] S TAP [9] S UCL [14] U TUE [32] U LSP [44] 2 1.30 S AR [36] 1.00 S OPS [43]  Low Random image erasing MixUp [47] Low Random image blending CutMix [46] Low Random image cutting and stitching Gaussian (used in [23])0 1 S
Data preprocessingLow Image blurring with a Gaussian kernel BDR (used in [23]) Low Image bit-depth reduction Gray (used in [23]) Low Image grayscale transformation JPEG (used in [23]) Low Image compression AVATAR [7] High Image corruption and restorationEarly stopping [35] Training-phase defense Low Finding peak validation accuracy UEraser-Lite [30] Low Stronger data augmentations UEraser-Max [30] High Adversarial augmentations AT [25] High Adversarial training metrics of different attacks and defense combinations, and can also perform qualitative visual analyses to help understand the characteristics of the datasets.Our benchmark currently includes 9 supervised and 2 unsupervised availability poisoning attacks, 9 defense algorithms, and 4 traditional image augmentation methods. In Table 1 and Table 2, we provide a brief summary of the properties of attack and defense algorithms. More detailed descriptions for each algorithm are provided in Appendix B.
EVALUATIONSDatasets We evaluated our benchmark on 4 commonly used datasets (CIFAR-10 [20], CIFAR-100 [20], SVHN [27], and an ImageNet [5] subset) and 5 mainstream models (ResNet-18 [15], ResNet-50 [15], MobileNetV2 [34], and DenseNet-121 [16]). To ensure a fair comparison between attack and defense methods, we used only the basic version of training for each model. Appendix A summarizes the specifications of the datasets and the test accuracies achievable through standard training on clean training data, and further describes the detail specifications of each dataset.
Attacks and defensesWe evaluated combinations of availability poisoning attacks and defense methods introduced in Section 3. Moreover, we explored 5 different data poisoning rates and 5 different models. In addition, We also explore two availability poisonings for unsupervised learning (UCL [14] and TUE [32]) and evaluate them on the recently proposed defenses (Gray, JPEG, Early stopping (ES), UEraser-Lite [30], and AVATAR [7]). The implementation details of all algorithms and additional results can be found in Appendix B.
Types of Threat ModelsWe can classify adversarial attacks based on three distinct availability poisoning threat models: ∞ -bounded attacks (DC, NTGA, EM, REM, TAP, and HYPO); 2 -bounded attacks (LSP and AR); an 0 -bounded attack (OPS). Given that 0 perturbations resist disruption from image preprocessing or augmentations and remain unaffected by ∞ adversarial training, the 0 -bounded OPS attack demonstrates robustness against a plethora of defenses. Conversely, in terms of stealthiness, the 0 attacks are less subtle than their ∞ and 2 counterparts, as illustrated in Figure 1 Training settings We trained the CIAFR-10, CIFAR-100 and ImageNet-subset models for 200 epochs and the SVHN models for 100 epochs. We used the stochastic gradient descent (SGD) optimizer with a momentum of 0.9 and a learning rate of 0.1 by default. As for unsupervised learning, all experiments are trained for 500 epochs with the SGD optimizer. The learning rate is 0.5 for SimCLR [1] and 0.3 for MoCo-v2 [2]. Please note that we generate sample-wise perturbations for all availability poisoning attacks. Specific settings for each defense method may have slight differences, and detailed information can be found in the Appendix C.
Standard ScenarioTo start, we consider a common scenario where both the surrogate model and target model are ResNet-18, and the poisoning rate is set to 100%. We first evaluate the performance of the supervised poisoning methods against 4 state-of-the-art defense mechanisms and 4 commonly used data augmentation strategies. Table 3 presents the evaluation results on CIFAR-10 from our benchmark. It is evident that the conventional data augmentation methods appear to be ineffective against all poisoning methods. Yet, even simple image compression methods (BDR, grayscale, and JPEG corruption from ISS [23]) demonstrate a notable effect in mitigating the poisoning attacks, but fails to achieve high clean accuracy. Despite requiring more computational cost or additional resources (pretrained diffusion models for AVATAR), methods such as UEraser-Max [30] and AVATAR [7], generally surpass the image compression methods from ISS in terms of effectiveness. While AVATAR is inferior to UEraser-Max in gaining accuracy, it decouples the defense into an independent data sanitization phase, allowing it to be directly used in all existing training scenarios. While the early stopping (ES) method can be somewhat effective as a defense, is not usually considered a good one. mainly due to the fact that the peak accuracy of the availability poisoning is not ideal. Adversarial training appears effective but in many cases is outperformed by even a simple JPEG compression, it also fails notably against OPS, as the ∞ perturbation budget cannot mitigate 0 threats. We further conduct experiments on the CIFAR-100, SVHN, and ImageNet-subset datasets, and the results are shown in Table 4.Our findings indicate that perturbations constrained by traditional p norms are ineffective against adversarial augmentation (UEraser-Max), and image restoration by pretrained diffusion models (AVATAR), as they break free from the assumption of p constraints. Even simple image compression techniques (JPEG, Grayscale, and BDR) can effectively remove the effect of perturbations. At this stage, availability poisoning attacks that rely on p -bounded perturbations may not be as effective as initially suggested by the relevant attacks.
CHALLENGING SCENARIOSTo further investigate the effectiveness and robustness of availability poisoning attacks and defenses, we conducted evaluations in more challenging scenarios. We considered partial poisoning scenarios, larger perturbation poisoning, and the attack transferability to different models.
Partial poisoningIn realistic scenarios, it is difficult for an attacker to achieve modification of the entire dataset. We thus investigate the impact of poisoning rate on the performance of availability poisoning. Figure 3 presents the results on CIFAR-10 and ResNet-18, w.r.t. each poisoning rate for attack-defense pairs, where each subplot corresponds to a specific poisoning attack method. We explore four different poisoning rates (20%, 40%, 60%, 80%).
Privacy protection under partial poisoningAs can be seen in Figure 3, the test accuracy of the model in the case of partial poisoning is only slightly lower than that in the case of a completely   clean dataset. This raises the following question: Are APAs effective in protecting only a portion of the training data? To answer, we introduce poisoning perturbations with APAs to a varying portion of the training data, and investigate how well the models learn the origin features that exist in the poisoned images for different poisoning rates. For this, Figure 4 evaluates and compares the mean losses of the unlearnable images used during training ("Unlearnable"), the origin images of the unlearnable part ("Clean"), and for reference, the mean losses of images unseen by the model from the test set ("Test"), and "Train" means the loss of the clean part of the training set. We find that the losses on the original images of the unlearnable part is similar to that of the test set, or even lower. This suggests that the availability poisoning perturbations can reasonably protect the private data against undefended learning. For a similar comparison of accuracies, please refer to Appendix C.1.
Larger perturbationsWe increased the magnitude of perturbations in availability poisoning attacks to further evaluate the performance of attacks and defenses. Table 5 presents the results of availability poisoning with larger perturbations on CIFAR-10. Due to such significant perturbations, their stealthiness is further reduced, making it challenging to carry out such attacks in realistic scenarios. However, larger perturbations indeed have a more pronounced impact on suppressing defense performance, leading to significant accuracy losses for all defense methods. There exists a trade-off between perturbation magnitude and accuracy recovery. Considering that at larger perturbations, availability poisoning is dramatically less stealthy, and some defense methods are still effective, it is not recommended to use larger perturbations.        Adaptive poisoning We evaluated strong adaptive poisons against various defenses using two poisoning methods, EM [17] and REM [10]. We assume that the defenders can be adapted to three defenses (Gray, JPEG, and UEraser), by using the attack in the perturbation generation process. From Tables 7 and8, it can be seen that adaptive poisoning significantly affects the performance of the Gray defense, but has less effect on JPEG and UEraser.
Unlearnable OriginalUnseen TrainUnsupervised learning We evaluated the availability poisoning attacks targeting unsupervised models on CIFAR-10. We considered two popular unsupervised learning frameworks: SimCLR [1] and MoCo-v2 [2]. All defense methods were applied before the data augmentation process, which means they were applied to preprocessed images before undergoing different data augmentations. Therefore, we only applied UEraser-Lite as a data preprocessing method. The results of all experiments are shown in Table 9. Visual analyses We provide visualization tools (Grad-CAM [39] and Shapley value maps [24]) to facilitate the analysis and understanding of availability poisoning attacks. We also use t-SNE [41] to visualize the availability poisons (Figure 7). Although t-SNE cannot accurately represent highdimensional spaces, it aids in the global visualization of feature representations, allowing us to observe specific characteristics of availability poisons. For additional discussions on the visualizations, please refer to Appendix C.3.Future outlook Future research directions on APAs should explore methods that enhance the resilience of perturbations. One approach to consider is the development of generalizable attacks, which can simultaneously target the DNNs being trained, diffusion models for image restoration, and remain robust against traditional or color distortions, among others. On the other hand, semanticbased perturbations offer an alternative strategy, as such modifications to images can be challenging to remove by defenses.
CONCLUSIONSWe have established the first comprehensive and up-to-date benchmark for the field of availability poisoning, covering a diverse range of availability poisoning attacks and state-of-the-art defense algorithms. We have conducted effective evaluations and analyses of different combinations of attacks and defenses, as well as additional challenging scenarios. Through this new benchmark, our primary objective is to provide researchers with a clearer understanding of the current progress in the field of availability poisoning attacks and defenses. We hope it can enable rapid comparisons between existing methods and new approaches, while also inspiring fresh ideas through our comprehensive benchmark and analysis tools. We believe that our benchmark will contribute to the advancement of availability poisoning research and the development of more effective methods to safeguard privacy.
REPRODUCIBILITY STATEMENTWe provide an open-source implementation of all attacks and defenses in the supplementary material. Following the README file, users can run all experiments on their own device to reproduce the results shown in paper.
ETHICS STATEMENTSimilar to many other technologies, the implementation of availability poisoning algorithms can be used by users for both beneficial and malicious purposes. We understand that these poisoning attack methods were originally proposed to protect privacy, but they can also be used to generate maliciously data to introduce model backdoors. The benchmark aims to promote an understanding of various availability poisoning attacks and defense methods, as well as encourage the development of new algorithms in this field. It is also important for us to raise awareness of the false sense of security provided by availability poisoning attacks. However, we emphasize that the use of these algorithms and evaluation results should comply with ethical guidelines and legal regulations. We encourage users to be aware of the potential risks of the technology and take appropriate measures to ensure its beneficial use for both society and individuals.• Transferable unlearnable examples (TUE) [32]: TUE discovers that UCL is effective only in unsupervised learning, while its performance significantly deteriorates in supervised learning. Therefore, TUE is proposed that simultaneously targets both supervised and unsupervised learning. Different to UCL, it additionally embeds linear separable poisons into unsupervised unlearnable examples using the class-wise separability discriminant.Defenses:• Adversarial training (AT) [25]: AT is a widely-recognized effective approach against availability poisoning. Small adversarial perturbations are applied to the training images during training, in order to improve the robustness of the model against perturbations. • Image Shortcut Squeezing (ISS) [23]: ISS uses traditional image compression techniques such as grayscale transformation, bit-depth reduction (BDR), and JPEG compression, as defenses against availability poisoning. • Early stopping (ES) [35]: Early stopping can quickly achieve peak accuracy on availability poisons, but due to the difference in behavior of various poisons, it fails to achieve favorable defense results. • Adversarial augmentations (UEraser) [30]: UEraser-Lite uses an effective augmentation pipeline to suppress availability poisoning shortcuts. UEraser-Max further improves the defense against availability poisoning through adversarial augmentations.• AVATAR [7]: Following DiffPure [28], AVATAR cleans the images of the unlearnable perturbations with diffusion models.
C EXPERIMENTAL SETTINGS AND ADDITIONAL RESULTSTable 11 presents the default hyperparameters for all availability poisoning attacks implemented in APBench.
C.1 PARTIAL POISONINGIn addition to the discussion on partial poisoning in Section 4, we provide the results in terms of accuracies in Figure 5.      
Unlearnable OriginalUnseen TrainFormally, consider asource dataset comprising original examples D clean = {(x 1 , y 1 ), . . . , (x n , y n )} where x i ∈ X denotes an input image and y i ∈ Y represents its label. The objective of the attacker is thus to construct a set of availability perturbations δ, such that models trained on the set of availability poisoned examples D poi (δ) = {(x + δ x , y) | (x, y) ∈ D clean } are expected to perform poorly when evaluated on a test set D test sampled from the distribution S:
Figure 1 :1Figure 1: Visualizations of unlearnable CIFAR-10 images with corresponding perturbations. Perturbations are normalized for visualization.
3 A3UNIFIED AVAILABILITY POISONING BENCHMARKAs shown in Figure2, APBench consists of three main components: (a) The availability poisoning attack module. This library includes a set of representative availability poisoning attacks that can generate unlearnable versions of a given clean dataset. (b) The poisoning defense module. This module integrates a suite of state-of-the-art defenses that can effectively mitigate the unlearning effect and restore clean accuracies to a certain extent. (c) The evaluation module. This module can efficiently analyze the performance of various availability poisoning attack methods using accuracy metrics and visual analysis strategies.
Figure 2 :2Figure 2: The overall system design of APBench.
Figure 3 :3Figure 3: The efficacy in test accuracies (%, vertical axes) of defenses (No defense, Grayscale, JPEG, and UEraser-Max) against different partial poisoning attacks including EM (a), REM (b), LSP (c), and AR (d) with poisoning ratios (horizontal axes) ranging from 20% to 80%.
Figure 4 :4Figure 4: The mean losses (vertical axes) indicate that original features in unlearnable examples are not learned by the model. All evaluations consider partial poisoning scenarios (poisoning rates from 20% to 80%, horizontal axes). Note that "Unlearnable" and "Original" respectively denote the set of unlearnable examples, and their original clean variants, "Train" means the loss of the clean part of the training set. and "Unseen" denote images from the test set unobserved during model training.
Figure 5 :5Figure5: The accuracies (%, vertical axes) indicate that original features in unlearnable examples are not learned by the model. All evaluations consider partial poisoning scenarios (poisoning rates from 20% to 80%, horizontal axes). Note that "Unlearnable" and "Original" respectively denote the set of unlearnable examples, and their original clean variants, and "Unseen" denote images from the test set unobserved during model training.
Table 1 :1Availability poisoning attack algorithms implemented in APBench. "Type" and "Budget" respectively denotes the type of perturbation and its budget. "Mode" denotes the training mode, where "S" and "U" and respectively mean supervised and unsupervised training. "No surrogate" denotes whether the attack requires access to a surrogate model for perturbation generation. "Class-wise" and "Sample-wise" indicate if the attack supports class-wise and sample-wise perturbation generation. "Stealthy" denotes whether the attack is stealthy to human.Attack Method Type Budget Mode No surrogate Class-wise Sample-wise StealthyDC [8]SNTGA [45]∞8/255
Table 2 :2Availability poisoning defense algorithms implemented in APBench.Defense MethodTypeTime Cost DescriptionStandardLowRandom image cropping and flippingCutOut [6]Data augmentations
. Perturbations bounded by both ∞ and 2 are comparable w.r.t. the degree of visual stealthiness and effectiveness. Importantly, the two 2 -bounded attacks (LSP and AR) do not require surrogate model training, and are thus more efficient in the unlearnable examples synthesis.
Table 3 :3Test accuracies (%) of models trained on poisoned CIFAR-10 datasets. The model trained on a clean CIFAR-10 dataset attains an accuracy of 94.32%.Method Standard CutOut CutMix MixUp Gaussian BDR Gray JPEGESU-Max AVATARATDC15.1919.9417.9125.0716.1067.73 85.55 83.57 26.0892.1782.1076.85EM20.7818.7922.2831.1414.7137.94 92.03 80.72 25.3993.6175.6282.51REM17.4721.9626.2243.0721.8058.60 92.27 85.44 31.3292.4382.4277.46HYPO70.3869.0467.1274.2562.1774.82 63.35 85.21 70.5288.4485.9481.49NTGA22.7613.7812.9120.5919.9559.32 70.41 68.72 28.1986.7886.2269.70TAP6.279.8814.2115.467.8870.75 11.01 84.08 39.5479.0587.7579.92LSP13.0614.9617.6918.7718.6153.86 64.70 80.14 29.1092.8376.9081.38AR11.7410.9512.6014.1513.8336.14 35.17 84.75 44.2990.1288.6081.15OPS14.6952.9864.7249.2713.3837.32 19.88 78.48 38.2077.9966.1614.95
Table 4 :4Test accuracies (%) on poisoned CIFAR-100, SVHN and ImageNet-subset datasets.DatasetMethod Standard CutOut CutMix MixUp Gaussian BDR Gray JPEGESU-MaxEM3.034.153.986.462.9934.10 59.14 58.71 7.0668.81CIFAR-100REM LSP3.73 2.564.00 2.333.71 4.5210.90 4.863.59 1.7129.16 57.47 55.60 10.99 27.12 39.45 52.82 9.5267.72 68.31AR1.871.633.172.352.6231.15 16.13 54.73 26.5855.95EM10.3313.3810.7712.798.8236.65 65.66 86.14 13.4790.24SVHNREM LSP14.02 12.1618.92 12.989.55 8.1719.56 18.867.54 7.1542.52 19.59 90.58 19.61 26.67 16.90 84.06 12.9188.26 90.64AR19.2314.926.7113.527.7539.24 10.00 92.46 89.3290.07EM2.944.054.734.153.156.45 12.20 31.73 8.8044.07ImageNet-100REM3.664.134.783.944.284.033.95 40.98 17.1942.14LSP38.5240.5629.787.8542.6826.58 25.18 36.83 39.5263.28No defenseGrayscaleJPEGUEraser-Max
Table 5 :5Test accuracies (%) on poisoned CIFAR-10 datasets with increased perturbations.Method BudgetNo defense Gray JPEGESU-MaxATEM∞ = 16/25518.7476.76 55.96 27.3988.0977.82REM∞ = 16/25519.8083.65 80.07 33.0780.3675.64LSP2 = 1.7415.8337.60 42.83 27.3087.2077.92AR2 = 1.5011.2026.10 78.24 20.9668.4270.14
Table 6 :6Clean test accuracies of different CIFAR-10 target models, where attacks are oblivious to the model architectures. Note that AR and LSP are surrogate-free, and for EM and REM the surrogate model is ResNet-18.Attack transferability across models In real-world scenarios, availability poisoning attackers can only manipulate the data and do not have access to specific details of the defender. Therefore, we conducted experiments on different model architectures. It is worth noting that all surrogate-based attack methods are considered using ResNet-18. The results are shown in Table6. It is evident that all surrogate-based and -free poisoning methods exhibit strong transferability, while the three recently proposed defenses also achieve successful defense across different model architectures. The only exception is the AR method, which fails against CaiT-small.ModelClean Method No defense Gray JPEGESU-Max AVATAREM14.4183.40 76.88 26.6985.8977.64ResNet-5094.47REM LSP16.26 19.2387.26 75.79 31.37 68.94 73.24 32.7392.69 93.0883.68 76.47AR11.8327.51 80.24 28.6681.4086.39EM13.6086.03 79.35 16.3583.2774.22SENet-1894.83REM LSP20.99 18.5484.50 78.92 22.85 65.06 76.51 26.3893.17 92.5384.37 75.19AR13.6834.26 79.29 37.0475.0684.37EM15.6277.21 70.96 16.7182.7175.62MobileNetV294.62REM LSP20.83 16.8280.81 72.27 21.92 61.07 72.03 28.1291.03 92.1082.77 76.81AR13.3628.54 68.14 39.4573.4081.63EM13.8982.49 78.42 15.6882.3776.69DenseNet-121 95.08REM LSP21.45 18.9485.47 78.42 22.35 67.95 74.90 26.8693.09 93.4783.04 78.22AR13.4325.51 81.12 36.5182.3689.92EM21.4780.42 72.64 30.9174.2954.84ViT-small84.66REM LSP32.17 29.0679.65 74.92 43.07 59.34 68.07 32.6983.27 87.0173.57 66.74AR25.0438.90 74.77 45.5463.9078.64EM17.0164.76 63.75 39.6963.3741.94CaiT-small71.96REM LSP26.11 25.0865.05 66.43 47.39 63.06 57.15 37.9572.05 70.9262.53 51.39AR68.6366.27 69.30 67.4170.0462.77
Table 7 :7Test accuracies (%) of adaptive poisoning with EM on ResNet-18.MethodStandard Gray JPEG U-MaxEM + Gray19.4821.64 78.3990.52EM + JPEG20.6790.29 76.2593.22EM + UEraser35.2488.62 80.4689.55
Table 8 :8Test accuracies (%) of adaptive poisoning with REM on ResNet-18.MethodStandard Gray JPEG U-MaxREM + Gray16.7056.33 82.4791.37REM + JPEG19.4591.71 75.8492.53REM + UEraser21.6189.26 77.5191.84
Table 9 :9Performance of availability poisoning attacks and defense on different unsupervised learning algorithms and datasets. Note that "U-Lite" denotes UEraser-Lite.Algorithm Method No Defense Gray JPEG U-Lite AVATARSimCLRUCL TUE47.25 57.1046.91 66.76 56.37 67.5468.42 66.5983.22 84.24MoCo-v2UCL TUE53.78 66.7353.34 65.44 64.95 67.2872.13 74.8283.08 82.48
Table 15 :15Detailed test accuracies (%) of models trained on poisoned CIFAR-10 datasets. This table is an extension of Table3, and further includes an error range of 3 separate runs for each experiment. 19±1.87 19.94±1.90 17.91±2.66 25.07±2.74 16.10±1.07 67.73±2.77 85.55±2.04 83.57±2.61 92.17±0.97 82.10±2.20 76.85±1.79 EM 20.78±1.03 18.79±2.66 22.28±2.47 31.14±3.51 14.71±0.66 37.94±2.64 92.03±0.37 80.72±1.22 93.61±0.48 75.62±2.16 82.51±1.24 REM 17.47±2.04 21.96±3.72 26.22±2.86 43.07±4.36 21.80±0.94 58.60±2.35 92.27±0.29 85.44±2.10 92.43±0.61 82.42±1.47 77.46±1.66 HYPO 70.38±1.79 69.04±1.33 67.12±3.27 74.25±2.60 62.17±1.03 74.82±2.18 63.35±0.88 85.21±1.35 88.44±0.71 85.94±2.06 81.49±2.37 NTGA 22.76±0.67 13.78±0.75 12.91±1.22 20.59±2.41 19.95±1.26 59.32±1.96 70.41±2.67 68.72±3.37 86.78±1.64 86.22±2.07 69.70±2.66 TAP 6.27±0.48 9.88±0.71 14.21±2.14 15.46±1.77 7.88±0.66 70.75±1.46 11.01±0.67 84.08±2.36 79.05±1.04 87.75±2.52 79.92±1.87 LSP 13.06±0.74 14.96±1.02 17.69±1.37 18.77±2.12 18.61±0.82 53.86±2.40 64.70±3.29 80.14±2.51 92.83±1.27 76.90±1.09 81.38±1.92 AR 11.74±0.37 10.95±0.70 12.60±1.02 14.15±1.28 13.83±1.70 36.14±2.04 35.17±1.77 84.75±2.27 90.12±1.89 88.60±2.33 81.15±1.94 OPS 14.69±0.55 52.98±2.49 64.72±2.70 49.27±2.66 13.38±0.31 37.32±1.87 19.88±0.79 78.48±0.94 77.99±1.30 66.16±2.13 14.95±0.67ATAVATARU-MaxJPEGGrayBDRGaussianMixUpCutMixCutOutStandard15.MethodDC