Title: Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks
Venue: NeurIPS 2023
Accepted: yes
Keywords: Machine unlearning

new attack vector

Camouflaging poisoning attacks
Abstract: We introduce camouflaged data poisoning attacks, a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An adversary first adds a few carefully crafted points to the training dataset such that the impact on the model's predictions is minimal. The adversary subsequently triggers a request to remove a subset of the introduced points at which point the attack is unleashed and the model's predictions are negatively affected. In particular, we consider clean-label targeted attacks (in which the goal is to cause the model to misclassify a specific test point) on datasets including CIFAR-10, Imagenette, and Imagewoof. This attack is realized by constructing camouflage datapoints that mask the effect of a poisoned dataset. We demonstrate efficacy of our attack when unlearning is performed via retraining from scratch, the idealized setting of machine unlearning which other efficient methods attempt to emulate, as well as against the approximate unlearning approach of Graves et al. (2021).

Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks ∗

Abstract

We introduce camouflaged data poisoning attacks , a new attack vector that arises in the context of machine unlearning and other settings when model retraining may be induced. An adversary first adds a few carefully crafted points to the training dataset such that the impact on the model's predictions is minimal. The adversary subsequently triggers a request to remove a subset of the introduced points at which point the attack is unleashed and the model's predictions are negatively affected. In particular, we consider clean-label targeted attacks (in which the goal is to cause the model to misclassify a specific test point) on datasets including CIFAR-10, Imagenette, and Imagewoof. This attack is realized by constructing camouflage datapoints that mask the effect of a poisoned dataset. We demonstrate the efficacy of our attack when unlearning is performed via retraining from scratch, the idealized setting of machine unlearning which other efficient methods attempt to emulate, as well as against the approximate unlearning approach of Graves et al. [2021].

1 Introduction

Machine Learning (ML) research traditionally assumes a static pipeline: data is gathered, a model is trained once and subsequently deployed. This paradigm has been challenged by practical deployments, which are more dynamic in nature. After initial deployment more data may be collected, necessitating additional training. Or, as in the machine unlearning setting [Cao and Yang, 2015], we may need to produce a model as if certain points were never in the training set to begin with. 1

While such dynamic settings clearly increase the applicability of ML models, they also make them more vulnerable. Specifically, they open models up to new methods of attack by malicious actors aiming to sabotage the model. In this work, we introduce a new type of data poisoning attack on models that unlearn training datapoints. We call these camouflaged data poisoning attacks .

The attack takes place in two phases (Figure 1). In the first stage, before the model is trained, the attacker adds a set of carefully designed points to the training data, consisting of a poison set and a camouflage set. The model's behaviour should be similar whether it is trained on either the training data, or its augmentation with both the poison and camouflage sets. In the second phase, the attacker triggers an unlearning request to delete the camouflage set after the model is trained. That is, the

model must be updated to behave as though it were only trained on the training set plus the poison set. At this point, the attack is fully realized, and the model's performance suffers in some way.

While such an attack could harm the model by several metrics, in this paper, we focus on targeted poisoning attacks - that is, poisoning attacks where the goal is to misclassify a specific point in the test set. Our contributions are the following:

We introduce camouflaged data poisoning attacks, demonstrating a new attack vector in dynamic settings including machine unlearning (Figure 1).

We realize these attacks in the targeted poisoning setting, giving an algorithm based on the gradient-matching approach of Geiping et al. [2021]. In order to make the model behavior comparable to as if the poison set were absent, we construct the camouflage set by generating a new set of points that undoes the impact of the poison set. We thus identify a new technical question of broader interest to the data poisoning community: Can one nullify a data poisoning attack by only adding points?

We demonstrate the efficacy of these attacks on a variety of models (SVMs and neural networks) and datasets (CIFAR-10 [Krizhevsky, 2009], Imagenette [Howard, 2019], and Imagewoof [Howard, 2019]).

Step 1

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1.1 Preliminaries

Machine Unlearning. A significant amount of legislation concerning the 'right to be forgotten' has recently been introduced by governments around the world, including the European Union's General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Canada's proposed Consumer Privacy Protection Act (CPPA). Such legislation requires organizations to delete information they have collected about a user upon request. A natural question is whether that further obligates the organizations to remove that information from downstream machine learning models trained on the data - current guidances [Information Commissioner's Office, 2020] and precedents [Federal Trade Commission, 2021] indicate that this may be the case. This goal has sparked a recent line of work on machine unlearning [Cao and Yang, 2015].

The simplest way to remove a user's data from a trained model is to remove the data from the training set, and then retrain the model on the remainder (also called 'retraining from scratch'). This is the ideal way to perform data deletion, as it ensures that the model was never trained on the datapoint of

Poisons

Camouflages

concern. The downside is that retraining may take a significant amount of time in modern machine learning settings. Hence, most work within machine unlearning has studied fast methods for data deletion, sometimes relaxing to approximately removing the datapoint. A related line of work has focused more on other implications of machine unlearning, particularly the consequences of an adaptive and dynamic data pipeline [Gupta et al., 2021, Marchant et al., 2022]. Our work fits into the latter line: we show that the potential to remove points from a trained model can expose a new attack vector. Since retraining from scratch is the ideal result that other methods try to emulate, we focus primarily on unlearning by retraining from scratch, but the same phenomena should still occur when any effective machine unlearning algorithm is applied. For example, we also demonstrate efficacy against the approximate unlearning method of Graves et al. [2021].

Data Poisoning. In a data poisoning attack, an adversary in some way modifies the training data provided to a machine learning model, such that the model's behaviour at test time is negatively impacted. Our focus is on targeted data poisoning attacks , where the attacker's goal is to cause the model to misclassify some specific datapoint in the test set. Other common types of data poisoning attacks include indiscriminate (in which the goal is to increase the test error) and backdoor (where the goal is to misclassify test points which have been adversarially modified in some small way).

The adversary is typically limited in a couple ways. First, it is common to say that they can only add a small number of points to the training set. This mimics the setting where the training data is gathered from some large public crowdsourced dataset, and an adversary can contribute a few judiciously selected points of their own. Other choices may include allowing them to modify or delete points from the training set, but these are less easily motivated. Additionally, the adversary is generally constrained to clean-label attacks : if the introduced points were inspected by a human, they should not appear suspicious or incorrectly labeled. We comment that this criteria is subjective and thus not a precise notion, but is nonetheless common in data poisoning literature, and we use the term as well.

2 Discussion of Other Related Work

The motivation for our work comes from Marchant et al. [2022], who propose a novel poisoning attack on unlearning systems. As mentioned before, the primary goal of many machine unlearning systems is to 'unlearn' datapoints quickly, i.e., faster than retraining from scratch. Marchant et al. [2022] craft poisoning schemes via careful noise addition, in order to trigger the unlearning algorithm to retrain from scratch on far more deletion requests than typically required. While both our work and theirs are focused on data poisoning attacks against machine unlearning systems, the adversaries have very different objectives. In our work, the adversary is trying to misclassify a target test point, whereas they try to increase the time required to unlearn a point.

In targeted data poisoning, there are a few different types of attacks. The simplest form of attack is label flipping , in which the adversary is allowed to flip the labels of the examples [Barreno et al., 2010, Xiao et al., 2012, Paudice et al., 2018]. Another type of attack is watermarking , in which the feature vectors are perturbed to obtain the desired poisoning effect [Suciu et al., 2018, Shafahi et al.,

2018]. In both these cases, noticeable changes are made to the label and feature vector, respectively, which would be noticeable by a human labeler. In contrast, clean label attacks attempt to make unnoticeable changes to both the feature vector and the label, and are the gold standard for data poisoning attacks [Huang et al., 2020, Geiping et al., 2021]. Our focus is on both clean-label poison and camouflage sets. While there are also works on indiscriminate [Biggio et al., 2012, Xiao et al., 2015, Muñoz-González et al., 2017, Steinhardt et al., 2017, Diakonikolas et al., 2019b, Koh et al., 2022] and backdoor [Gu et al., 2017, Tran et al., 2018, Sun et al., 2019] poisoning attacks, these are beyond the scope of our work, see Goldblum et al. [2020], Cinà et al. [2022] for additional background on data poisoning attacks.

Cao and Yang [2015] initiated the study of machine unlearning through exact unlearning, wherein the new model obtained after deleting an example is statistically identical to the model obtained by training on a dataset without the example. A probabilistic notion of unlearning was defined by Ginart et al. [2019], which in turn is inspired from notions in differential privacy [Dwork et al., 2006]. Several works studied algorithms for empirical risk minimization (i.e., training loss) [Guo et al., 2020, Izzo et al., 2021, Neel et al., 2021, Ullah et al., 2021, Thudi et al., 2022, Graves et al., 2021, Chourasia et al., 2022], while later works study the effect of machine unlearning on the generalization loss [Gupta et al., 2021, Sekhari et al., 2021]. In particular, these works realize that unlearning data points quickly can lead to a drop in test loss, which is the theme of our current work. Several works have considered implementations of machine unlearning in several contexts starting with the work of Bourtoule et al. [2021]. These include unlearning in deep neural networks [Golatkar et al., 2020, 2021, Nguyen et al., 2020], random forests [Brophy and Lowd, 2021], large scale language models [Zanella-Béguelin et al., 2020], the tension between unlearning and privacy [Chen et al., 2021, Carlini et al., 2022], anomaly detection [Du et al., 2019], insufficiency of preventing verbatim memorization for ensuring privacy [Ippolito et al., 2022], and even auditing of machine unlearning systems [Sommer et al., 2020].

After the first version of our paper was uploaded on arXiv, Yang et al. [2022] discovered defenses against the data poisoning procedure of Geiping et al. [2021]. One may further use their techniques to defend against our camouflaged poisoning attack as well, however, we note that this does not undermine the contributions of this paper. Our main contribution is to introduce this new kind of attack, and bring to attention that machine unlearning enables this attack; the specific choice of which procedure is used for poison generation or camouflage generation is irrelevant to the existence of such an attack.

3 Setup and Algorithms

3.1 Threat Model and Approach

The camouflaged poisoning attack takes place through interaction between an attacker and a victim , and is triggered by an unlearning request. We assume that the attacker has access to the victim's model architecture, 2 the ability to query gradients on a trained model (which could be achieved, e.g., by having access to the training dataset), and a target sample that it wants to attack. The attacker first sets the stage for the attack by introducing poison samples and camouflage samples to the training dataset, which are designed so as to have minimal impact when a model is trained with this modified dataset. At a later time, the attacker triggers the attack by submitting an unlearning request to remove the camouflage samples. The victim first trains a machine learning model (e.g., a deep neural network) on the modified training dataset, and then executes the unlearning request by retraining the model from scratch on the left over dataset. The goal of the attacker is to change the prediction of the model on a particular target sample ( x tar , y tar ) previously unseen by the model during training from y tar to a desired label y adv , while still ensuring good performance over other validation samples. Formally, the interaction between the attacker and the victim is as follows (see Figure 1) :

The attacker introduces a small number of poisons samples S po and camouflage samples S ca to a clean training dataset S cl . Define S cpc = S cl + S po + S ca .

Victim trains an ML model (e.g., a neural network) on S cpc , and returns the model θ cpc .

The attacker submits a request to unlearn the camouflage samples S ca . 3

The victim performs the request, and computes a new model θ cp by retraining from scratch on the left over data samples S cp = S cl + S po .

Note that the attack is only realized in Step 4 when the victim executes the unlearning request and retrains the model from scratch on the left over training samples. In fact, in Steps 1-3, the victim's model should behave similarly to as if it were trained on the clean samples S cl only. In particular, the model θ cpc will predict y tar on x tar , whereas the updated model θ cp will predict y adv on x tar . Both models should have comparable validation accuracy. Such an attack is implemented by designing a camouflage set that cancels the effects of the poison set while training, but retraining without the camouflage set (to unlearn them) exposes the poison set, thus negatively affecting the model.

Before we delve into technical details on how the poisons and camouflages are generated, we will provide an illustrative scenario where such an attack can take place. Suppose an image-based social media platform uses ML-based content moderation to filter out inappropriate images, e.g., adult content, violent images, etc., by classifying the posts as 'safe' and 'unsafe.' An attacker can plant a camouflaged poisoning attack in the system in order to target a famous personality, like a politician, a movie star, etc. The goal of the attacker is to make the model misclassify a potentially sensitive target image of that person (e.g., a politically inappropriate image) as 'safe.' However, the attacker does not want to unleash this misclassification immediately, but to instead time it to align with macro events like elections, the release of movies, etc. Thus, at a later time when the attacker wishes, the misclassification can be triggered making the model classify this target image as safe and letting it circulate on the platform, thus hurting the reputation of that person at an unfortunate time (e.g., before an election or before when their movie is released). The attack is triggered by submitting an unlearning request.

We highlight that camouflaged attacks may be more dangerous than traditional data poisoning attacks, since camouflaged attacks can be triggered by the adversary. That is, the adversary can reveal the attack whenever it wishes by submitting an unlearning request. On the other hand, in the traditional poisoning setting, the attack is unleashed as soon as the model is trained and deployed, the timing of which the attacker has little control over.

In order to be undetectable, and represent the realistic scenario in which the adversary has limited influence on the model's training data, the attacker is only allowed to introduce a set of points that is much smaller than the size of the clean training dataset (i.e., ∣ S po ∣ ≪ ∣ S cl ∣ and ∣ S ca ∣ ≪ ∣ S cl ∣ ). Throughout the paper and experiments, we denote the relative size of the poison set and camouflage set by b p ∶= ∣ S po ∣ ∣ S cl ∣ × 100 and b c ∶= ∣ S ca ∣ ∣ S cl ∣ × 100 , respectively. Additionally, the attacker is only allowed to generate poison and camouflage samples by altering the base images by less than ε distance in the ℓ ∞ norm (in our experiments ε ≤ 16 , where the images are represented as an array of pixels in 0 to 255 ). Thus, the attacker executes a so-called clean-label attack, where the corrupted images would be visually indistinguishable from original base images and thus would be given the same label as before by a human data validator. We parameterize a threat model by the tuple ( ε, b c , b p ) .

3.2 Poison and Camouflage Samples

The attacker implements the attack by first generating poison samples, and then generating camouflage samples to cancel their effects, as shown in the following.

Poison samples. Poison samples are designed so that a network trained on S cp = S cl + S po predicts the label y adv (instead of y tar ) on a target image x tar . While there are numerous data poisoning attacks in the literature, we adopt the state-of-the-art procedure of Geiping et al. [2021] for generating poisons due to its high success rate, efficiency of implementation, and applicability across various models. However, our framework is flexible: in principle, other attacks for the same setting could

serve as a drop-in replacement, e.g., the methods of Aghakhani et al. [2021] or Huang et al. [2020], or any method introduced in the future. 4

Suppose that S cp consist of N 1 samples ( x i , y i ) i ≤ N 1 out of which the first P samples with index i = 1 to P belong to the poison set S po . 5 The poison samples are generated by adding small perturbations ∆ i to the base image x i so as to minimize the loss on the target with respect to the adversarial label, which can be formalized as the following bilevel optimization problem 6

and we define the constraint set Γ ∶= { ∆ ∶ ∥ ∆ ∥ ∞ ≤ ε and ∆ i = 0 for all i > P } . The objective function in (1) is called the adversarial loss [Geiping et al., 2021]. In all our experiments, we generate the poison points using the gradient matching technique of Geiping et al. [2021], which we detail in Appendix A for completeness.

Camouflage samples. Camouflage samples are designed to cancel the effect of the poisons, such that a model trained on S cpc = S cl + S po + S ca behaves identical to the model trained on S cl , and makes the correct prediction on x tar . We formulate this task via a bilevel optimization problem similar to (1). Let S cpc consist of N 2 samples ( x j , y j ) j ≤ N 2 out of which the last C samples with index j = N 2 -C + 1 to N 2 belong to the camouflage set S ca . The camouflage points are generated by adding small perturbations ∆ j to the base image x j so as to minimize the loss on the target with respect to the adversarial label. In particular, we find the appropriate ∆ by solving:

and we define the constraint set Γ ∶= { ∆ ∶ ∥ ∆ ∥ ∞ ≤ ε and ∆ j = 0 for all j ≤ N 2 -C } .

3.3 Efficient Algorithms for Generating Camouflages

Given the poison images, camouflage images are designed in order to neutralize the effect of the poisons. Here, we give intuition into what we mean by canceling the effect of poisons, and provide two procedures for generating camouflages efficiently: label flipping, and gradient matching.

3.3.1 Camouflages via Label Flipping

Suppose that the underlying task is a binary classification problem with the labels y ∈ {-1 , 1 } , and that the model is trained using linear loss ℓ ( f ( x, θ ) , y ) = -yf ( x, θ ) . Then, simply flipping the labels allows one to generate a camouflage set for any given poison set S po . In particular, S ca is constructed as: for every ( x i , y i ) ∈ S po , simply add ( x i , -y i ) to S ca (i.e., b p = b c ). It is easy to see that for such camouflage samples, we have for any θ ,

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We can also similarly show that the gradients (as well as higher order derivatives) are equal, i.e., ∇ θ ∑ S cpc ℓ ( f ( x, θ ) , y ) = ∇ θ ∑ S cl ℓ ( f ( x, θ ) , y ) for all θ . Thus, training a model on S cpc is equivalent to training it on S cl . In essence, the camouflages have perfectly canceled out the effect of the poisons. We validate the efficacy of this approach via experiments on linear SVM trained with hinge loss (which resembles linear loss when the domain is bounded). See Section 4.1.1 for details.

While label flipping is a simple and effective procedure to generate camouflages, it is fairly restrictive. Firstly, label flipping only works for binary classification problems trained with linear loss. Secondly, the attack is not clean label as the camouflage images are generated as ( x i , -y i ) by giving them the opposite label to the ground truth, which can be easily caught by a validator. Lastly, the attack is vulnerable to simple data purification techniques by the victim, e.g., the victim can protect themselves by preprocessing the data to remove all the images that have both the labels ( y = + 1 and y = -1 ) in the training dataset. In the next section, we provide a different procedure to generate clean-label camouflages that works for general losses and multi-class classification problems.

3.3.2 Camouflages via Gradient Matching

Here, we discuss our main procedure to generate camouflages, which builds on the gradient matching idea of Geiping et al. [2021]. Note that, our objective in (2) is to find ∆ such that when the model is trained with the camouflages, it minimizes the original-target loss in (2) (with respect to the original label y tar ) thus making the victim model predict the correct label on this target sample. Since, (2) is computationally intractable, one may instead try to implicitly minimize the original-target loss by finding a ∆ such that for any model parameter θ ,

The above equation suggests that minimizing (e.g., using Adam / SGD) on camouflage samples will also minimize the original-target loss, and thus automatically ensure that the model predicts the correct label on the target. Unfortunately, finding perturbations that satisfy the above is also intractable as the approximate equality is required to hold for all θ . Building on Geiping et al. [2021], we relax this condition to be satisfied only for a fixed model θ cp -the model trained on the dataset S cp = S cl + S po , and obtain the perturbations which minimize the cosine-similarity loss given by

Implementation details. We minimize (3) using the Adam optimizer [Kingma and Ba, 2015] with a fixed step size of 0 . 1 . In order to increase the robustness of camouflage generation, we do R restarts (where R ≤ 10 ). In each restart, we first initialize ∆ randomly such that ∥ ∆ ∥ ∞ ≤ ε and perform M steps of Adam optimization to minimize ψ ( ∆ , θ cp ) . Each optimization step only requires a single differentiation of the objective ψ with respect to ∆ , and can be implemented efficiently. After each step, we project back the updated ∆ into the constraint set Γ so as to maintain the property that ∥ ∆ ∥ ∞ ≤ ε . After doing R restarts, we choose the best round by finding ∆ ∗ with the minimum ψ ( ∆ ⋆ , θ cp ) .

4 Experimental Evaluation

We generate poison samples by running Algorithm 2 (given in the appendix), and camouflage samples by running Algorithm 1 with R = 1 and M = 250 . 7 Each experiment is repeated K times by setting a different seed each time, which fixes the target image, poison class, camouflage class, base poison images and base camouflage images. Due to limited computation resources, we typically set K ∈ { 3 , 5 , 8 , 10 } depending on the dataset and report the mean and standard deviation across different trials. We say that poisoning was successful if the model trained on S cp = S cl + S po predicts the label y adv on the target image. Furthermore, we say that camouflaging was successful if the model trained on S cpc = S po + S cl + S ca predicts back the correct label y tar on the target image, provided that poisoning was successful. A camouflaged poisoning attack is successful if both poisoning and camouflaging were successful.

Algorithm 1 Gradient Matching to generate camouflages

Require: Network f (⋅ ; θ cp ) trained on S cl + S po , the target ( x tar , y tar ) , Camouflage budget C , perturbation bound ε , number of restarts R , optimization steps M

1: Collect a dataset S ca = { x j , y j } C j = 1 of C many images whose true label is y tar .

2: for r = 1 , . . . R restarts do

3: Randomly initialize perturbations ∆ s.t. ∥ ∆ ∥ ∞ ≤ ε .

4: for k = 1 , . . . , M optimization steps do

5: Compute the loss ψ ( ∆ , θ cp ) as in (5) using the base camouflage images in S ca .

6: Update ∆ using an Adam update to minimize ψ , and project onto the constraint set Γ .

7: end for

8: Amongst the R restarts, choose the ∆ ∗ with the smallest value of ψ ( ∆ ∗ , θ cp ) .

9: end for

10: Return the poisoned set S ca = { x j + ∆ j ∗ , y j } C j = 1 .

4.1 Evaluations on CIFAR-10

CIFAR-10 [Krizhevsky, 2009] is a multiclass classification problem with 10 classes, with 6,000 color images in each class of size 32 × 32 . We follow the standard split into 50,000 training images and 10,000 test images.

4.1.1 Support Vector Machines

In order to perform evaluations on SVM, we first convert the CIFAR-10 dataset into a binary classification dataset (which we term as Binary-CIFAR-10) by merging the 10 classes into two groups: animal ( y = + 1 ) and machine ( y = -1 )). Images (in both training and test datasets) that were originally labeled ( bird, cat, deer, dog, frog, horse ) are relabeled animal , and the remaining images, with original labels ( airplane, cars, ship, truck ) , are labeled machine .

We train a linear SVM (no kernel was used) with the hinge loss. We evaluate both label flipping and gradient matching to generate camouflages, and different threat models ( ε, b p , b c ) ; the results are reported in Table 1. Training details and hyperparameters are given in Appendix B.3. Each poison and camouflage generation took about 40 - 50 seconds (for b p = b c = 0 . 2% ) . Each trained model had validation accuracy of around 81.63% on the clean dataset S cl , which did not change significantly when we retrained after adding poison samples and/or camouflage samples. Note that the efficacy of the camouflaged poisoning attack was more than 70% in most of the experiments. We give examples of the generated poisons/camouflages in Appendix B.5.

4.1.2 Neural Networks

We perform extensive evaluations on the (multiclass) CIFAR-10 classification task with various popular large-scale neural networks models including VGG-11, VGG-16 [Simonyan and Zisserman, 2015], ResNet-18, ResNet-34, ResNet-50 [He et al., 2016], and MobileNetV2 [Sandler et al., 2018], trained using cross-entropy loss. Details on training setup and hyperparameters are in Appendix B.3.

We report the efficacy of our camouflaged poisoning attack across different models and threat models ( ε, b p , b c ) in Figure 3; also see Appendix B.3 for detailed results. Each model was trained to have

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validation accuracy between 81-87% (depending on the architecture), which changed minimally when the model was retrained with poisons and/or camouflages. Camouflaging was successful at least 70% of the time for VGG-11, VGG-16, Resnet-18, and Resnet-34 and about 30% of the time for MobileNetV2 and Resnet-50.

Note that even a small attack success rate represents a credible threat: first, these attack success rates are comparable with those of Geiping et al. [2021], and thus future or undisclosed targeted data poisoning attacks are likely to be more effective. Second, since these success rates are over the choice of the attacker's random seed, the attacker can locally repeat the attack several times and deploy a successful one. Therefore, low success rates do not imply a fundamental barrier, and can be overcome by incurring a computational overhead - by repeating an attack 10x, even an attack with only 20% success rate can be boosted to ≈ 90% success rate.

4.2 Evaluations on Imagenette and Imagewoof

We evaluate the efficacy of our attack vector on the challenging multiclass classification problem on the Imagenette and Imagewoof datasets [Howard, 2019], both of which are subsets consisting of 10 classes from the Imagenet dataset [Russakovsky et al., 2015] with about 900 images per class.

We evaluate our camouflaged poisoning attack on two different neural network architectures-VGG-16 and ResNet-18 (trained with cross entropy loss), and different threat models ( ε, b p , b c ) listed in Table 2. More details about the dataset, experiment setup and hyperparameters are provided in Appendix B.4. In our experiments, camouflaging was successful for at least 50% of the time when poisoning was successful. However, because we modified about 13% of the training dataset when adding poisons/camouflages, we observe that the fluctuation in the model's validation accuracy can be up to 7%, which is expected since we make such a large change in the training set.

4.3 Additional Experiments

Ablation experiments. In the appendix, we provide the additional experiments on CIFAR-10, showing that:

( a ) Our attack is robust to random deletions of generated poison and camouflage samples during evaluation (Appendix B.6.2), and to training with data augmentation.

( b ) Our attack successfully transfers across different models, i.e., when the victim model is different from the model on which poison and camouflage samples were generated. (Appendix B.6.3)

( c ) Our attack is successful across different threat models i.e., different values of b p and b c (Appendix B.6.1).

( d ) The poison and camouflage samples generated in our experiments have similar feature space distributions, and thus data sanitization defenses (e.g., Diakonikolas et al. [2019a]) based on feature distributions will not succeed in removing the generated poison and camouflage samples (Appendix B.6.5).

Approximate unlearning. In all our experiments so far, the victim retrains a model from scratch (on the leftover training data) to fulfil the unlearning request, i.e., the victim performs exact unlearning. In the past few years there has been significant research effort in developing approximate unlearning algorithms under various structural assumptions (e.g., convexity [Sekhari et al., 2021, Guo et al., 2020]), or under availability of large memory [Bourtoule et al., 2021, Brophy and Lowd, 2021, Graves et al., 2021], etc.), and one may wonder whether attacks are still effective under approximate unlearning. Unfortunately, most existing approximate unlearning approaches are not efficient (either requiring strong assumptions or taking too much memory) for large-scale deep learning settings considered in this paper, and thus we were unable to evaluate our attack against these methods with our available resources. However, we provide evalulations against the Amnesiac Unlearning algorithm of Graves et al. [2021], which we were able to implement ( Appendix B.6.6). We note that our attack continues to be effective even against this approximate unlearning method.

5 Discussion and Conclusion

We demonstrated a new attack vector, camouflaged poisoning attacks , against machine learning pipelines where training points can be unlearned . This shows that as we introduce new functionality to machine learning systems, we must be aware of novel threats that emerge. We outline a few extensions and future research directions:

Our method for generating poison and camouflage points was based on the gradient-matching attack of Geiping et al. [2021]. However, the attack framework could accommodate effectively any method for targeted poisoning, e.g., the methods of Aghakhani et al. [2021] or Huang et al. [2020], or any method introduced in the future. We provide preliminary experiments in Appendix C of camouflaged-poisoning attacks developed using the Bullseye Polytope technique from Aghakhani et al. [2021], and show that the attack continues to succeed.

While we only performed experiments with a single target point, similar to Geiping et al. [2021] (and several other works in the targeted data poisoning literature), the proposed attack extends straightforwardly to a collection of targets (rather than a single target). This can be done by calculating the gradient of multiple (instead of a single) target images and using it in the gradient matching in (5) and (3).

In order to generate poisons and camouflages, our approach needs the ability to query gradients of a trained model at new samples, thus the attack is not black-box. While the main contribution of the paper was to expose that machine unlearning (which is a new and emerging but still underdeveloped technology) can lead to a new kind of vulnerability called a camouflaged poisoning attack, performing such an attack in a black-box setting is an interesting research question.

While we could not verify our attack against other approximate unlearning methods in the literature [Bourtoule et al., 2021, Brophy and Lowd, 2021, Sekhari et al., 2021, Guo et al., 2020] due to lack of resources needed to implement them, we believe that our attack should be effective against any provable approximate unlearning approach. This is because the objective of approximate unlearning is to output a model that is statistically-indistinguishable from the model retrained-from-scratch on the remaining data, and the experiments provided in the paper directly evaluate on the retrained-from-scratch model (which is the underlying objective that all approximate unlearning methods aim to emulate). Verifying the success of our approach against other approximate unlearning approaches is an interesting future research direction.

We considered a two-stage process in this paper, with one round of learning and one round of learning. It would also be interesting to explore what kinds of threats are exposed by even more dynamic systems where points can be added or removed in an online fashion.

Finally, it is interesting to determine what other types of threats can be camouflaged, e.g., indiscriminate [Lu et al., 2022] or backdoor poisoning attacks [Chen et al., 2017, Saha et al., 2020]. Beyond exploring this new attack vector, it is also independently interesting to understand how one can neutralize the effect of an attack by adding points.

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A Gradient Matching for Efficient Poison Generation [Geiping et al., 2021]

In this section, we discuss the key intuition of Geiping et al. [2021] for efficient poison generation. Our objective is to find perturbations ∆ such that when the model is trained on the poisoned samples, it minimizes the adversarial loss in (1) thus making the victim model predict the wrong label y adv on the target sample. However, directly solving (1) is computationally intractable due to bilevel nature of the optimization objective. Instead, one may implicitly minimize the adversarial loss by finding a ∆ such that for any model parameter θ ,

In essence, (4) implies that gradient based minimization (e.g., using Adam / SGD) of the training loss on poisoned samples also minimizes the adversarial loss. Thus, training a model on S cl + S po will automatically ensure that the model predicts y adv on the target sample. Unfortunately, computing ∆ that satisfies (4) is also intractable as it is required to hold for all values of θ . The key idea of Geiping et al. [2021] to make poison generation efficient is to relax (4) to only be satisfied for a fixed model θ cl -the model obtained by training on the clean dataset S cl . To implement this, Geiping et al. [2021] minimize the cosine-similarity loss between the two gradients defined as:

Geiping et al. [2021] demonstrated that (5) can be efficiently optimized for many popular large-scale machine learning models and datasets. We provide the pseudocode in Algorithm 2.

Algorithm 2 Gradient Matching to generate poisons [Geiping et al., 2021]

Require: Clean network f (⋅ ; θ clean ) trained on uncorrupted base images S cl , a target ( x tar , y tar ) and an adversarial label y adv , Poison budget P , perturbation bound ε , number of restarts R , optimization steps M

1: Collect a dataset S po = { x i , y i } P i = 1 of P many images whose true label is y adv .

2: for r = 1 , . . . R restarts do

3: Randomly initialize perturbations ∆ s.t. ∥ ∆ ∥ ∞ ≤ ε .

4: for k = 1 , . . . , M optimization steps do

5: Compute the loss ϕ ( ∆ , θ clean ) as in (5) using the base poison images in S po .

6: Update ∆ using an Adam update to minimize ϕ , and project onto the constraint set Γ .

7: end for

8: Amongst the R restarts, choose the ∆ ∗ with the smallest value of ϕ ( ∆ ∗ , θ clean ) .

9: end for

10: Return the poisoned set S po = { x i + ∆ i ∗ , y i } P i = 1 .

B Implementation Details

B.1 Code

We provide code for our experiments as ready-to-deploy Jupyter notebooks. The code to work with different dataset can be found in:

SVM_Binay_cifar10.ipynb : Experiments for Binary-CIFAR-10 dataset with linear SVM.

cifar10.ipynb : Experiments for CIFAR-10 dataset with various neural network models.

imagenette.ipynb : Experiments for Imagenette / Imagewoof dataset with various neural network models.

We plan to make our code public with the final version of the paper.

B.2 Experimental Setup

For the ease of replication, we report the corresponding poison class, target class, camouflage class and Target ID for various seeds in different experiments.

B.3 Additional Details on CIFAR-10 Experiments

B.3.1 SVM Experiments

We train the linear SVM (no kernel was used) with the hinge loss: ℓ ( f ( x, θ ) , y ) = max { 0 , 1 -yf ( x, θ )} . The training was done using the svm.LinearSVC class from Scikit -learn [Pedregosa et al., 2011] on a single CPU. In the pre-processing stage, each image in the training dataset was

normalized to have ℓ 2 -norm 1 . Each training on Binary-CIFAR-10 dataset took 25 - 30 seconds. In order to generate the poison points, we first use torch.autograd to compute the cosine-similarity loss (5), and then optimize it using Adam optimizer with learning rate 0.001. Each poison and camouflage generation took about 40 - 50 seconds (for b p = b c = 0 . 2% ) . We evaluate both label flipping and gradient matching to generate camouflages, and different threat models ( ε, b p , b c ) ; the results are reported in Table 6. For each of our experiments we chose K = 10 seeds of the form 'kkkkkk" where k ∈ { 0 , . . . , 9 } and the seed 99999. Each trained model had validation accuracy of around 81.63% on the clean dataset S cl , which did not change significantly when we retrained after adding poison samples and / or camouflage samples. Note that the efficacy of the camouflaged poisoning attack was more than 70% in most of the experiments. We provide a sample of the generated poisons and camouflages in Figure 9 in Appendix B.5.

B.3.2 Support Vector Machines

In order to perform evaluations on SVM, we first convert the CIFAR-10 dataset into a binary classification dataset (which we term as Binary-CIFAR-10) by merging the 10 classes into two groups: animal ( y = + 1 ) and machine ( y = -1 )). Images (in both training and test datasets) that were originally labeled ( bird, cat, deer, dog, frog, horse ) are relabeled animal , and the remaining images, with original labels ( airplane, cars, ship, truck ) , are labeled machine .

We train a linear SVM (no kernel was used) with the hinge loss. We evaluate both label flipping and gradient matching to generate camouflages, and different threat models ( ε, b p , b c ) ; the results are reported in Table 6. Each poison and camouflage generation took about 40 - 50 seconds (for b p = b c = 0 . 2% ) . Each trained model had validation accuracy of around 81.63% on the clean dataset S cl , which did not change significantly when we retrained after adding poison samples and/or camouflage samples. Note that the efficacy of the camouflaged poisoning attack was more than 70% in most of the experiments. We give examples of the generated poisons/camouflages in Appendix B.5.

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B.3.3 Neural Network Experiments

Implementation Details and Hyperparameters. Each model is trained with cross-entropy loss ℓ ( f ( x, θ ) , y ) = -log ( Pr ( y = f ( x, θ ))) on a single GPU using PyTorch [Paszke et al., 2019], and using mini-batch SGD with weight decay 5e-4, momentum 0.9, learning rate 0.01, batch size 100, and 40 epochs over the training dataset. Each training run took about 45 minutes. The poison and camouflage sets were generated using gradient matching by first defining the cosine-similarity

loss using torch.autograd and then minimizing it using Adam with a learning rate of 0.1. Each poison/camouflage generation took about 1.5 hours. 8

Further Results. We next elaborate on the results reported in Figure 3. In Table 7, we report the efficacy of the proposed camouflaged poisoning attack on different neural network architectures where the threat model is given by ε = 16 , b p = 0 . 6% , b c = 0 . 6% . The reported results are an average over 5 seeds from 2000000000-2000001111. In the first column under attack success, we report the number of times poisoning was successful amongst the run trials, and in the second column, we report the number of times camouflaging was successful for the trials for which poisoning was successful.

In Table 8, we report the success of the proposed attack when we change the threat model, but fix the network architecture to be ResNet-18. Each experiment was repeated times 5 times with 8 restarts each time, and the mean success rate is reported. These experiments were conducted with 5 seeds from 2000011111-2111111111.

B.4 Additional Details on Imagenette / Imagewoof Experiments

We evaluate the efficacy of our attack vector on the challenging multiclass classification problem on the Imagenette and Imagewoof datasets [Howard, 2019]. Imagenette is a subset of 10 classes ( Tench, English springer, Cassette player, Chain saw, Building/church, French horn, Truck, Gas pump, Golf ball, Parachute ) from the Imagenet dataset [Russakovsky et al., 2015]. The Imagenette dataset consists of around 900 images of various sizes for each class. In total, we have 13394 images which are divided into a training dataset of size 9469 and test dataset of size 3925. To perform training, all images are resized and centrally cropped down to 224 × 224 pixels.

Imagewoof [Howard, 2019] is another subset of Imagenet dataset consisting of 10 classes ( Shih-Tzu, Rodesian Ridgeback, Beagle, English Foxhound, Border Terrier, Austrailian Terrier, Golden Retriever,

Old English Sheep Dog, Samoyed, Dingo ) . Imagewoof consists of around 900 images of various sizes for each class, and in total 12954 images which are divided into a training dataset of size 9025 and test dataset of size 3929. Similar to Imagenette, we resize all images and crop to the central 224 × 224 pixels before training.

We evaluate our camouflaged poisoning attack on two different neural network architectures-VGG-16 and ResNet-18, and different threat models ( ε, b p , b c ) listed in Table 2 . Each model is trained on a single GPU with cross-entropy loss, that is minimized using SGD algorithm with weight decay 5e-4, momentum 0.9 and batch size 20. We start with a learning rate of 0.01, and exponentially decay it with γ = 0 . 9 after every epoch, for a total of 50 epochs over the training dataset. The poisons and camouflages were generated using gradient matching by first defining the cosine-similarity loss using torch.autograd and then optimizing it using Adam optimizer with learning rate 0.1.

Target

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B.5 Visualizations

STIHL

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Camouflages

B.6 Additional Experiments

B.6.1 Ablation Study for Different Values of b p and b c

In this section, we explore the effect of changing the relative sizes of the poison samples and camouflage samples. The experiments are performed on ResNet-18 for CIFAR-10 dataset with b p = 1% and b c = { 0 . 5 , 1 , 1 . 5 } % respectively, and vice-versa. We report the results in Table 9.

One may also wonder what is the price that an attacker has to pay in terms of the success rate of a poisoning (only) attack if it chooses to devote a part of the budget for camouflages. In Table 10, we report the results for comparing the success rate 1% poisons and 1% camouflages, to success rate with 2% poisons (and no camouflages.)

B.6.2 Robustness of the Proposed Attack to Random Deletions

We next explore the effect of random removal of the generated poison and camouflage samples on the success of our attack. In a data-scraping scenario, a victim may not scrape all the data points modified by an attacker. In Table 11, we report the effect on attack success when different amounts of the generated poison and camouflage samples are deleted uniformly at random.

Table 11: Effect of random removal of the generated poison and camouflage samples on the success of the proposed camouflaged poisoning attack on CIFAR-10 dataset trained on ResNet-18, with ε = 16 . The reported success rates are averaged over 5 different trials with seeds 200001111 - 211111111. For each experiment, we do 4 restarts for every poison and camouflage generation.

B.6.3 Transfer Experiments

In this section, we show that the poison and camouflage samples generated by the proposed approach transfer across models. Thus, an attacker can successfully execute the camouflaged poisoning attack, even if the victim trains a different model than the one on which the poison and camouflage samples were generated. We show the transfer success in Figure 10. The brewing network denotes the network architecture on which poison and camouflage samples were generated (we adopt the same notation as Geiping et al. [2021]). The victim network denotes the model architecture used by the victim for training on the manipulated dataset.

We ran a total of 3 experiments per (brewing model, victim model) pair using the seeds 20000000002000000011. Each reported number denotes the fraction of times when both poisoning and camouflaging were successful in the transfer experiment, and thus the attack could take place.

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B.6.4 Robustness to Data Augmentation

Data augmentation is commonly used to avoid overfitting in deep neural networks. In order to be applicable in the real life, our poisoning and camouflaging attacks must be successful even when the model is trained with data augmentation. In order to validate this, we evaluate our approach on CIFAR10 dataset trained with data augmentation on ResNet-18 in the threat model ε = 16 , b p = b c = 1% ; the results are in Table 12. The considered data augmentations are:

No Augmentation : Exact images from the training dataset are used.

Augmentation Set 1 : 50% chance that the image will be horizontally flipped, but no rotations.

Augmentation Set 2 : 50% chance that the image will be horizontally flipped, and random rotations in Uniform (-10 , 10 ) degrees.

The reported results in Table 12 are an average over 5 random seeds from 'kkkkkk" where 1 ≤ k ≤ 5 . As expected, the validation accuracy for the model trained on clean dataset increased from 82%

percent when trained without augmentation, to 86% for augmentation Set 1 and 88% for augmentation set 2. The addition of data augmentation during training and re-training stages make it harder for poisoning to succeed and at the same time makes it easier for camouflaging to succeed.

B.6.5 Similarity of Feature Space Distance

A natural approach to defend against dataset manipulation attacks is to try to identify the modified images, and then remove them from the training dataset (i.e., data sanitization ), e.g. Diakonikolas et al. [2019a]. For instance, one could cluster images based on their distance from their class mean image, or from the target image. This type of defense could potentially thwart watermarking poisoning attacks such as Poison Frogs [Shafahi et al., 2018]. As we show in Figure 11, such a defense would not be effective against our proposed poison and camouflage generation procedures, as the data distribution for the poison set and the camouflage set is similar to that of the clean images from the respective classes.

L2Distance toTargetImageFeatures

0.030

total poison class

0.025

total poison class

0.025

poisons

0.020

poisons

0.020

Count

Count

0.015

0.015

0.010

0.010

0.005

0.005

0.000

40

60

80

100

0.000

40

60

80

100

120

140

L2Distance toCamouflage ClassMean

0.035

total camouflage class

0.030

camouflages

0.020

camouflage class

camouflages

0.025

0.020

Count

0.015

0.015

0.010

0.010

0.005

0.005

0.000

20

40

60

80

100

0.000

40

60

80

100

120

140

B.6.6 Approximate Unlearning

In the experiments reported so far, we assumed that the victim performs exact unlearning, i.e. retrains the model from scratch on the leftover training data every time there is an unlearning request. However, in many cases, retraining from scratch for every unlearning request could be computationally expensive. This has inspired a plethora of research over the past few years in machine unlearning to develop approximate unlearning methods that are computationally faster than retraining from scratch [Sekhari et al., 2021, Guo et al., 2020, Bourtoule et al., 2021, Brophy and Lowd, 2021, Graves et al., 2021]. One may wonder if our attack also succeeds when performing approximate unlearning. Unfortunately, the algorithms dicussed in the prior works are infeasible for our large-scale deep learning setting; they either require strong structural properties (e.g., convexity [Sekhari et al.,

2021, Guo et al., 2020]) or require access to large memory [Bourtoule et al., 2021, Brophy and Lowd, 2021, Graves et al., 2021], etc. Hence, we were unable to evaluable most of these methods. However, we were successful in implementing the approximate unlearning methods in Graves et al. [2021] called Amnesiac Unlearning withing our available resources and report the results in Table 13.

To give the high level idea of Amnesiac Unlearning , note that the final model produced by multi-epoch mini-batch SGD like algorithms is given by:

where ∆ θ e,b is the incremental update using batch b in epoch e . In Amnesiac Unlearning , the learner keeps track of which batches contain which sample points. When given an unlearning request, the learner computes the bathces SB that contain the sensitive data (that requested for removal) and updates the model as

The above update is followed by a few epochs of fine tuning on left over data samples to recover any loss in validation error. As reported in Graves et al. [2021], the above update method is particularly effective when the sensitive data comprises of about 1-2% of the training dataset, which is the case in our experiments. We refer the reader to Graves et al. [2021] for more details. The reported results in Table 13 were obtained by using the open source implementation of Amnesiac Unlearning by Graves et al. [2021]. We note that each experiment on CIFAR-10 took about 200GB of memory.

B.7 Multiple Targets Scenarios

In addition, we performed a limited number of experiments on poisoning and camouflaging multiple targets using the gradient matching method. The results (as shown in Table 14) are comparable to the experiments performed by [Geiping et al., 2021] on multiple targets. We observe that our attack continues to be effective under multiple target settings. Furthermore, choosing a method that is more effective under the multi-target setting may increase the success of our attack.

C Alternative Approaches to Generate Poisons and Camouflages

Thus far, our experiments have focused solely on generating poison and camouflage points using the gradient matching technique. In this section, we show that our attack framework is robust to the

poison generation method: we generate a camouflaged poisoning attack using Bullseye Polytope poisoning-a clean-label targeted attack proposed by Aghakhani et al. [2021]. We believe that similar results should hold for a variety of other data poisoning techniques.

Bullseye Polytope (BP) maximizes the similarity between representations of the poisons and target. In doing so, it implicitly minimizes the alignment between poison and target gradients with respect to the penultimate layer, which captures most of the gradient norm variation [Katharopoulos and Fleuret, 2019]. In the transfer learning scenario, the poisons are crafted to have a similar representation to that of the target. Here, a linear layer is trained on the poisoned data using the representations obtained from a pre-trained clean model. The gradient of the linear model is proportional to the representations learned by the pre-trained model. Therefore, by maximizing the similarity between the representations of the poisons and the adversarially labeled target, the attack indeed increases the alignment between their gradients [Yang et al., 2022].

In our experiments, a poison set and a camouflage set were crafted and evaluated in a white-box setting, where the method has the highest average attack success rate [Schwarzschild et al., 2021]. Two pre-trained ResNet-18 models are used as the feature extractor to generate adversarial examples and as the victim, respectively. We set an ℓ ∞ perturbation budget of ε = 7 and perform BP for 1,000 iterations each to obtain poisons and camouflages. We then use Adam with a learning rate of 0.1 to fine-tune the victim's linear classifier on the poisoned dataset for 60 epochs. Finally, we re-load the victim model, repeat the fine-tuning process with the poisoned + camouflaged dataset and compare the results.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel attack in machine unlearning. There are two step in the attack process. In the first step, the poisoned data and the camouflaged data are fed into the initial model training, together with the benign data. The machine unlearning process would take out the camouflaged data and the retraining from scratch process will be on the poisoned data and the benign data. Experiments are done to demonstrate the proposed attack.\n\nSTRENGTHS:\n•\tThe authors propose a novel attack in machine unlearning. \n\n•\tEvaluations are done on the label flipping and gradient matching setting for camouflaged data generation, two types of models (SVM and neural networks will multiple different network structures), and three datasets.\n\n•\tThe paper is well-written and easy to follow.\n\nWEAKNESSES:\n•\tThe paper makes impractical assumptions: \n\n        o\tIn step 4, the unlearned model is by training the clean ones and poisoned ones. This reviewer fails to understand if the initial training can identify the poisoning effect and require the camouflaged data to mask the poisoning effect, the why the machine unlearning procedure does not offer such capability, especially the authors adopt the retraining from scratch strategy to perform machine unlearning?\n\n•\tThe paper makes a few false claims:\n\n        o\tThis reviewer does not understand why the authors call the proposed approach clean-label when they have a poisoned set generated very similar to adversarial examples: looks the same for human but will be misclassified by the model. Even if the attack does not change the label directly, gradient matching would still change the optimization of the objective. Otherwise, if the label or the intermediate computation results is not changed and only the input data is slightly modified, the so-called attack becomes adversarial training.\n\n        o\tThe authors are making speculations which are not true. The authors keep emphasizing the proposed approach can be applied to approximate unlearning methods in the literature. But this reviewer does not buy this claim. For example, Bourtoule et al., 2021 would trace back to the intermediate models before the data to be removed is in use. This approach could fail the proposed attack.\n\n        o\tIn machine unlearning approach in this paper, which is retraining from scratch, is also a cost-heavy way to do machine unlearning. Therefore, the comments on 288-290 on the strong assumptions or taking too much memory is not as convincing.\n\n        o\tTypo: line 324, the second “learning” should be “unlearning”\n\nQUESTIONS:\nSee weakness.\n\nLIMITATIONS:\nNot applicable.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper studies poisoning attacks against machine unlearning systems, where the attacker injects a set of poisoning samples and a set of camouflage samples. When both sets of samples are present in the training set, the trained model performs similarly to the clean model but shows adversarial behaviors and misclassified a particular sample into a wrong label when the camouflage samples are deleted. Throurgh evaluations, the proposed attack can achieve reasonable poisoning success and the camouflaged samples also serve well in preserving correct predictions when present.\n\nSTRENGTHS:\n1. The idea of attacking machine unlearning systems is very interesting.\n2. The proposed attack, although majorly leverages the existing targeted poisoning attack, performs relatively effectively.\n3. Evaluations show that the proposed attack may transfer across different model architectures and also unlearning methods.\n\nWEAKNESSES:\n1. The extension to the practically relevant setting of misclassifying multiple test points is not demonstrated. \n2. The attack requires a significant amount of poisoning points to work for more complex datasets such as ImageNet.\n\nQUESTIONS:\nOverall, I think the problem studied in this paper is very interesting and the presentation of this paper is also very clear. However, I have the following concerns:\n1. Misclassifying a single test point might not be practically relevant, attackers might be interested in investing a significant amount of poisoning points to misclassify as many test samples as possible. Although the authors discussed about the extension is straightforward, for practical implementation, I believe this will not work well. The original GradMatch attack performs poorly when the target number of samples exceeds maybe 5 images, not to mention that the proposed attack already performs inferior to the GradMatch attack in the standard training setting. So, I believe the authors may either tune down the statement slightly or demonstrate it through experiments (this will be ideal). \n2. The authors mentioned that testing with different random seeds can lead to drastically different effectiveness and in practice, it is hard to know the random seed used by the victim (I hope I understand the paper correctly that the attacker and victim are indeed using different random seeds. Otherwise, the experiments probably be needed to rerun). In this regard, how can the attacker pick a good random seed to work well against the unknown seed by the victim? Repeating multiple times may not help as it is related to the unknown seed used by the victim. \n3. The attack performs relatively poorly on more complex ImageNet datasets, and the performance of the GradMatch attack in the standard training is not listed. By checking the original paper, it seems the attack success of GradMatch is > 80% at <1% poisoning ratio, which means there is a big performance gap between the original GradMatch and the GradMatch in unlearning settings. This is probably a more worrying observation for me-- if there is an inherent barrier to poisoning complex models in unlearning settings, then we may have to worry too much about the poisoning threat. I guess this issue cannot be solved in a short amount of time but would be ideal if the authors can provide the attack performance when the total poisoning ratio is set as the common ratio used in prior poisoning literature (e.g., 1%). Related to this, the poisoning budget is usually 2x the budget of the poisoning standard setting, while in practice, attackers only have a limited budget. This implies the poisoning threat in machine unlearning will be less severe than poisoning in standard training.  \n4. There is an observation that the attack effectiveness varies drastically across model architectures. Do the authors have explanations for this? \n5. When generating the camouflaged points, what is the intuition behind using the poisoned model $\\theta_{cp}$ to generate the points? Is this a possible reason that leads to inferior performance of the attack on more complex datasets?  \n\n====================================================\n\nThis is to acknowledge that I have read the authors rebuttals carefully and my concerns are addressed.\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new attack for security risks in the field of machine unlearning, namely the camouflaged data poisoning attack. The attacker adds poisoned and camouflaged points to the training data, and then triggers a deletion request to remove the camouflaged points, causing the model to misclassify the target points. The authors conduct an extensive evaluation to assess the performance of the proposed attack, and the results are promising.\n\nSTRENGTHS:\n- Popular topics\n- Well-structured and easy to follow\n\nWEAKNESSES:\n- Unrealistic assumptions\n- Needs more detailed explanations\n- Needs more complex datasets\n\nQUESTIONS:\nThe authors introduce a new attack surface in the context of machine unlearning called camouflaged data poisoning attacks. I like this topic because both data poisoning attacks and machine unlearning are two popular research topics. In addition, the authors clearly describe the threat model, methodology, and evaluation. I appreciate that.\n\nHowever, I do have the following concerns.\n\n- The authors design two algorithms for generating camouflage samples. The first one is through label flipping and the second one is through gradient matching. My concern is that the second scenario where an attacker has access to model gradients is unrealistic, especially in so-called image-based social media platforms. I do not believe that such a platform would have white-box access to the user. Although the authors discuss this point in the last section, I wish the authors would focus more on black-box settings.\n\n- Figure 1 provides the attack performance of the newly designed poisoning attack. Here, I wonder why the attack success rate on ResNet-50 is much lower than other model architectures. I hope the authors will have more discussion on this. I conjecture that one of the reasons behind this may be the much larger model complexity of ResNet-50. Therefore, I would further like the authors to add an ablation study on the impact of target model complexity.\n\n- Following the above, I am also curious about how the proposed attack performs on more complex datasets. Specifically, all experiments are conducted on 10-class datasets. However, in real-world scenarios, the task is likely to be more complex, with more than 10 classes. Therefore, I would like the authors to perform an investigation on more complex datasets, such as CIFAR-100 and ImageNet.\n\nLIMITATIONS:\nInsufficient experiments, conclusions may not generalize to complex architectures and datasets", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-07-05"}

comments: SUMMARY:
This paper introduces a very interesting novel attack vector for tampering with the behavior of a machine learning model by influencing its training dataset. It poses that the practice of "unlearning" examples that are requested to be taken down can be exploited. The attack proceeds in several steps: 1. The adversary poisons the training data with two types of poisons: camouflages and regular poisons. 2. The adversary asks that the camouflages be unlearned (as might be their right under some legislation). 3. Unlearning is applied by the model developers, which triggers the poisoning effect and some examples are misclassified at inference time of the "unlearned" model.

STRENGTHS:
This is a strong and novel attack.

WEAKNESSES:
I believe the biggest limitation of this work is that experiments are only conducted on small computer vision datasets. It would be very interesting to find out if these poisons work against modern text-to-image or large language models.

QUESTIONS:
No questions from me.

LIMITATIONS:
No concerns here.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 4
DATE: 2023-07-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a clean-label targeted data poisoning attack for machine unlearning pipelines. Gradient matching [A] is used to generate the poisons, and experiments use the retraining from scratch method for machine unlearning.\n\nThe core idea is for an attacker to introduce poisons and camouflages to the clean dataset. The camouflages mask the effect of poisons, so the victim model trained on the combined dataset (clean + poison + camouflage) doesn't exhibit poisoned behavior. After this, the attacker makes an unlearning request to remove the camouflages. After the unlearning process, the model exhibits the poisoned behavior. The poisoning aims to misclassify a specific test sample to a chosen target class.\n\n[A] Jonas Geiping, et al. “Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching.” International Conference on Learning Representations. 2021.\n\nSTRENGTHS:\nThe paper proposes an original attack idea – a targeted poisoning attack in a machine unlearning setup. \n\nThe optimization method used for generating the poisons (Gradient Matching) is a well-known method from poisoning literature to solve the bi-level problem. The experimental setup is decent with experiments covering a handful of datasets (CIFAR-10, ImageNette and Imagewoof) and machine learning models (SVMs, VGG, ResNets).\n\nI was intrigued by the question the paper poses, “We thus identify a new technical question of broader interest to the data poisoning community: Can one nullify a data poisoning attack by only adding points?” I agree that this direction of study might be of significant interest to the community.\n\nWEAKNESSES:\nThere are certain weaknesses in the claims of the paper, and the experiments are not thorough enough. They need improvement.\n\n1. (Line 216) “camouflaging was successful ...  provided that poisoning was successful. A camouflaged poisoning attack is successful if both poisoning and camouflaging were successful.” This explanation doesn't seem consistent with the numbers reported in the tables. If the camouflaging success rate is conditional on the poisoning being successful, I would think camouflaging success rate ≤ poisoning success rate. But that doesn't seem to be true in Row 3 of Table 1. Furthermore, if both these numbers are independent of each other, the tables should also report when both happen, as mentioned in the last line of the quoted text. \n\n2. (Footnote) “We diverge slightly from the threat model described above, in that the adversary modifies rather than\nintroduces new points. We do this for convenience and do not anticipate the results would qualitatively change.”  \nI do not think this is true. If we have a clean image and also the (same clean image + perturbation) in the poisoning dataset, wouldn't it change the results?\n\n3. (Amount of training – Line 244) “Each model was trained to have validation accuracy between 81-87% (depending on the architecture)”.\n81-87% on CIFAR-10 seems to be quite a low number. It should be easy to achieve 90-95% on CIFAR-10 with VGG-11, VGG-16, Resnet-18, and Resnet-34. These models seem to be under trained. Given that no experiments are performed on ImageNet and compute shouldn't be a bottleneck for CIFAR-10 training, the models need to be trained more so that the experimental results have more value for future research. See https://github.com/kuangliu/pytorch-cifar for reference numbers.\n\n4. (Extension to a set of targets) The attack threat model used here aims to target a single image from the test set. Though this is a starting point, the applicability of such a poison is quite limited. A natural extension would be to do this for a collection of targets. Though this is mentioned as future work in the discussion section, I would recommend that being a part of this current work.\n\nQUESTIONS:\nThere are some parts of the text which are not clear and will benefit with additional explanation.\n\n1. (Line 172) What is $b_p$ and $b_c$?\n2. (Line 173) What is $P$?\n3. (Line 265) What is the validation accuracy for these models. I do not see them mentioned in the main text / appendix.\n\nLIMITATIONS:\nA discussion on potential negative societal impact is missing. As this is a poisoning attack paper with implications on real-world deployments of machine learning models, I would recommend the authors to include a comment.", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2023-08-01"}