pith. sign in

arxiv: 2408.00929 · v2 · submitted 2024-08-01 · 💻 cs.LG · cs.CR

Verification of Machine Unlearning is Fragile

Binchi Zhang , Zihan Chen , Cong Shen , Jundong Li This is my paper

Pith reviewed 2026-05-23 22:22 UTC · model grok-4.3

classification 💻 cs.LG cs.CR
keywords machine unlearningverification strategiesadversarial unlearningdata privacymodel providersinformation retentionmachine learning
0
0 comments X

The pith

Model providers can bypass machine unlearning verification while retaining the data's information.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper asks whether model providers can claim to unlearn data yet keep its information and still pass checks from data owners. It divides existing verification strategies into two categories and shows each can be defeated by a dedicated adversarial process. Both processes are supported by theoretical analysis and tested on real-world datasets. The result is that verification cannot reliably confirm deletion has occurred. This undermines the transparency that unlearning is meant to provide.

Core claim

Verification of machine unlearning is fragile: model providers can apply two novel adversarial unlearning processes that circumvent both categories of verification strategies while retaining the information of data that was supposed to be unlearned.

What carries the argument

Two adversarial unlearning processes, one for each of the two categories of verification strategies, that allow retention of data information without detection.

If this is right

  • Data owners cannot rely on current verification to confirm their data has been removed from a model.
  • Model providers retain the ability to keep data information while claiming compliance with unlearning requests.
  • Both categories of verification strategies require redesign to handle adversarial retention of information.
  • Legislation that depends on verifiable unlearning loses effectiveness if these bypasses remain possible.
  • Machine unlearning as currently practiced does not guarantee removal of data influence from the model.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Companies may face stronger incentives to adopt the adversarial processes if verification remains weak.
  • Similar fragility could appear in other privacy tools that rely on post-hoc verification of data removal.
  • Verification methods should be developed jointly with unlearning algorithms rather than added afterward.
  • Empirical tests on larger models and different data modalities would clarify how general the bypasses are.

Load-bearing premise

Existing verification strategies fall into the two categories examined and model providers can run the adversarial processes without extra detection mechanisms catching them.

What would settle it

A verification method that flags the output of either adversarial process as non-compliant with proper unlearning on the same datasets used in the paper's experiments.

Figures

Figures reproduced from arXiv: 2408.00929 by Binchi Zhang, Cong Shen, Jundong Li, Zihan Chen.

Figure 1
Figure 1. Figure 1: The connection of our threat model and different verifi￾cation strategies. Our retraining method can deceive the backdoor and reproducing verification, and our forging method can only de￾ceive a subset of reproducing verification but with better efficiency. Devadas, 2016). With the trusted hardware, their framework provides a better safety guarantee for verifying MUL. Re￾cently, Eisenhofer et al. proposed … view at source ↗
Figure 2
Figure 2. Figure 2: An illustration of the retraining-based adversarial un￾learning framework. The PoRT is generated based on the retraining process where the mini-batch d (t) r ∈ D\Du sampling is guided by the similarity with d (t) ∈ D in gradient. while preserving the information of unlearned data. To sat￾isfy the reproducing verification with a 0 verification error for each updating function, the model provider has to fol￾… view at source ↗
Figure 3
Figure 3. Figure 3: An illustration of the forging-based adversarial unlearn￾ing framework. Different from the retraining-based adversarial method, the PoRT here is generated directly from the PoT recorded in the original training. w (t) r (with d (t) r ) is obtained by conducting the forging map over the PoT instead of using the model updating function g (t) r . the PoT to generate a valid PoRT instead of retraining. We form… view at source ↗
Figure 4
Figure 4. Figure 4: Verification error of forging-based adversarial unlearning method for MLP over MNIST, CNN over CIFAR-10, and ResNet over SVHN [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of execution time among original training, naive retraining, and adversarial unlearning methods over three real-world datasets. of adopted neural networks, 1e −3 can be seen as a small value. We can also see a reduction in the overall verification error as the model scale decreases (from ResNet to MLP). In addition, we also illustrate the mean value and the standard deviation of the verification… view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of two mini-batch selection strategies: random sampling Sr and nearest neighbor Sn [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of verification error under different learning rate configurations. B.3. Membership Inference Attack Membership Inference Attack (MIA) (Shokri et al., 2017) is seen as an effective evaluation method of machine unlearning by measuring the privacy leakage of the data supposedly unlearned. Different from the reproducing verification and the backdoor verification, we tend to categorize MIA into the … view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of gradient norm over three datasets. the predictions of the test samples are similar to the predictions of the training samples, and they are difficult to distinguish since the model learns well and has good generalizability (the predictions are correct and with high confidence). Hence, the attack accuracy is low. For difficult target tasks (e.g. CIFAR-10), the model might have a confident and … view at source ↗
read the original abstract

As privacy concerns escalate in the realm of machine learning, data owners now have the option to utilize machine unlearning to remove their data from machine learning models, following recent legislation. To enhance transparency in machine unlearning and avoid potential dishonesty by model providers, various verification strategies have been proposed. These strategies enable data owners to ascertain whether their target data has been effectively unlearned from the model. However, our understanding of the safety issues of machine unlearning verification remains nascent. In this paper, we explore the novel research question of whether model providers can circumvent verification strategies while retaining the information of data supposedly unlearned. Our investigation leads to a pessimistic answer: \textit{the verification of machine unlearning is fragile}. Specifically, we categorize the current verification strategies regarding potential dishonesty among model providers into two types. Subsequently, we introduce two novel adversarial unlearning processes capable of circumventing both types. We validate the efficacy of our methods through theoretical analysis and empirical experiments using real-world datasets. This study highlights the vulnerabilities and limitations in machine unlearning verification, paving the way for further research into the safety of machine unlearning.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper claims that verification of machine unlearning is fragile: model providers can circumvent existing verification strategies (categorized into two types) via two novel adversarial unlearning processes while still retaining information from the target data. The claim is supported by a categorization of strategies, theoretical analysis, and empirical experiments on real-world datasets.

Significance. If the central claim holds, the work is significant for highlighting vulnerabilities in machine unlearning verification mechanisms that are increasingly relevant for privacy compliance. Explicit strengths include the provision of both theoretical analysis and empirical validation on real-world datasets, which together offer concrete evidence rather than purely conceptual arguments.

major comments (1)
  1. [categorization section] Categorization of verification strategies (abstract and the section introducing the two types): the claim that these two categories encompass the main existing approaches is load-bearing for the generality of the circumvention result, yet the manuscript provides no systematic enumeration or literature survey justifying completeness of the partition.
minor comments (2)
  1. [abstract] The abstract states that the adversarial processes are validated 'through theoretical analysis' but does not reference the specific theorem, proposition, or derivation that establishes the circumvention property; adding such a pointer would improve traceability.
  2. Notation for the two adversarial processes should be introduced with explicit definitions (e.g., as functions or algorithms) at first use to avoid ambiguity when the processes are later compared to the verification categories.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive assessment of the paper's significance and the recommendation for minor revision. We address the single major comment below.

read point-by-point responses

  1. Referee: Categorization of verification strategies (abstract and the section introducing the two types): the claim that these two categories encompass the main existing approaches is load-bearing for the generality of the circumvention result, yet the manuscript provides no systematic enumeration or literature survey justifying completeness of the partition.

    Authors: We agree that an explicit justification of the partition's completeness would strengthen the generality claim. The two categories are motivated by the fundamental distinction in how verification queries access the model (output-only vs. requiring internal access or retraining), which we believe captures the dominant existing strategies. However, the current manuscript does not include a dedicated literature survey. In the revision we will add a subsection that enumerates representative works from the literature, maps them to the two categories, and discusses why alternative approaches (if any) fall outside the scope or reduce to one of the types. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper establishes its claim by categorizing existing verification strategies into two types, proposing two new adversarial unlearning processes, and validating them via independent theoretical analysis plus experiments on external real-world datasets. No equations, parameter fits, or self-citations are shown to reduce the central result to its own inputs by construction; the contribution rests on newly introduced methods rather than renaming or re-deriving prior results.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are described. The work appears to rest on standard assumptions in machine learning privacy research such as the existence of verification oracles and the ability to modify training processes adversarially.

pith-pipeline@v0.9.0 · 5724 in / 1031 out tokens · 43234 ms · 2026-05-23T22:22:20.591304+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. The Measure of Deception: An Analysis of Data Forging in Machine Unlearning

    cs.LG 2025-09 conditional novelty 8.0

    The Lebesgue measure of ε-forging sets decays as O(ε) or ε^d for linear models and as ε^{(d-r)/2} under mild regularity assumptions, with vanishing probability of random sampling.

  2. Can Vision Models Truly Forget? Mirage: Representation-Level Certification of Visual Unlearning

    cs.CV 2026-05 unverdicted novelty 7.0

    Mirage auditing reveals that VFL unlearning methods passing output-level checks still retain substantial class structure in representations across multiple datasets and baselines.

  3. Towards Reliable Forgetting: A Survey on Machine Unlearning Verification

    cs.LG 2025-06 unverdicted novelty 6.0

    A survey that organizes machine unlearning verification methods into behavioral and parametric categories and outlines open problems.

Reference graph

Works this paper leans on

53 extracted references · 53 canonical work pages · cited by 3 Pith papers

  1. [1]

    and Stich, S

    Ajalloeian, A. and Stich, S. U. On the convergence of sgd with biased gradients. arXiv preprint arXiv:2008.00051, 2020

    work page arXiv 2008

  2. [2]

    A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N

    Bourtoule, L., Chandrasekaran, V., Choquette-Choo, C. A., Jia, H., Travers, A., Zhang, B., Lie, D., and Papernot, N. Machine unlearning. In 2021 IEEE Symposium on Security and Privacy (SP), pp.\ 141--159, 2021

    work page 2021

  3. [3]

    Boyd, S. P. and Vandenberghe, L. Convex optimization. Cambridge university press, 2004

    work page 2004

  4. [4]

    and Yang, J

    Cao, Y. and Yang, J. Towards making systems forget with machine unlearning. In 2015 IEEE Symposium on Security and Privacy (SP), pp.\ 463--480, 2015

    work page 2015

  5. [5]

    California consumer privacy act

    CCPA. California consumer privacy act. 2018. URL https://oag.ca.gov/privacy/ccpa

    work page 2018

  6. [6]

    Fast federated machine unlearning with nonlinear functional theory

    Che, T., Zhou, Y., Zhang, Z., Lyu, L., Liu, J., Yan, D., Dou, D., and Huan, J. Fast federated machine unlearning with nonlinear functional theory. In International conference on machine learning, pp.\ 4241--4268, 2023

    work page 2023

  7. [7]

    When machine unlearning jeopardizes privacy

    Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., and Zhang, Y. When machine unlearning jeopardizes privacy. In Proceedings of the 2021 ACM SIGSAC conference on computer and communications security, pp.\ 896--911, 2021

    work page 2021

  8. [8]

    Graph unlearning

    Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., and Zhang, Y. Graph unlearning. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pp.\ 499--513, 2022

    work page 2022

  9. [9]

    Efficient model updates for approximate unlearning of graph-structured data

    Chien, E., Pan, C., and Milenkovic, O. Efficient model updates for approximate unlearning of graph-structured data. In International Conference on Learning Representations, 2023

    work page 2023

  10. [10]

    S., Tarun, A

    Chundawat, V. S., Tarun, A. K., Mandal, M., and Kankanhalli, M. Zero-shot machine unlearning. IEEE Transactions on Information Forensics and Security, 2023

    work page 2023

  11. [11]

    and Devadas, S

    Costan, V. and Devadas, S. Intel sgx explained. Cryptology ePrint Archive, 2016

    work page 2016

  12. [12]

    Idea: A flexible framework of certified unlearning for graph neural networks

    Dong, Y., Zhang, B., Lei, Z., Zou, N., and Li, J. Idea: A flexible framework of certified unlearning for graph neural networks. arXiv preprint arXiv:2407.19398, 2024

    work page arXiv 2024

  13. [13]

    Verifiable and provably secure machine unlearning

    Eisenhofer, T., Riepel, D., Chandrasekaran, V., Ghosh, E., Ohrimenko, O., and Papernot, N. Verifiable and provably secure machine unlearning. arXiv preprint arXiv:2210.09126, 2022

    work page arXiv 2022

  14. [14]

    Verifi: Towards verifiable federated unlearning

    Gao, X., Ma, X., Wang, J., Sun, Y., Li, B., Ji, S., Cheng, P., and Chen, J. Verifi: Towards verifiable federated unlearning. arXiv preprint arXiv:2205.12709, 2022

    work page arXiv 2022

  15. [15]

    General data protection regulation

    GDPR. General data protection regulation. 2016. URL https://gdpr-info.eu/

    work page 2016

  16. [16]

    Eternal sunshine of the spotless net: Selective forgetting in deep networks

    Golatkar, A., Achille, A., and Soatto, S. Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 9304--9312, 2020

    work page 2020

  17. [17]

    Mixed-privacy forgetting in deep networks

    Golatkar, A., Achille, A., Ravichandran, A., Polito, M., and Soatto, S. Mixed-privacy forgetting in deep networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 792--801, 2021

    work page 2021

  18. [18]

    Deep learning

    Goodfellow, I., Bengio, Y., and Courville, A. Deep learning. MIT press, 2016

    work page 2016

  19. [19]

    Certified data removal from machine learning models

    Guo, C., Goldstein, T., Hannun, A., and Van Der Maaten, L. Certified data removal from machine learning models. In International Conference on Machine Learning, pp.\ 3832--3842, 2020

    work page 2020

  20. [20]

    Verifying in the dark: Verifiable machine unlearning by using invisible backdoor triggers

    Guo, Y., Zhao, Y., Hou, S., Wang, C., and Jia, X. Verifying in the dark: Verifiable machine unlearning by using invisible backdoor triggers. IEEE Transactions on Information Forensics and Security, 2023

    work page 2023

  21. [21]

    Deep residual learning for image recognition

    He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 770--778, 2016

    work page 2016

  22. [22]

    A., Chaudhuri, K., and Zou, J

    Izzo, Z., Smart, M. A., Chaudhuri, K., and Zou, J. Approximate data deletion from machine learning models. In International Conference on Artificial Intelligence and Statistics, pp.\ 2008--2016, 2021

    work page 2008

  23. [23]

    and Woo, S

    Kim, J. and Woo, S. S. Efficient two-stage model retraining for machine unlearning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 4361--4369, 2022

    work page 2022

  24. [24]

    Koh, P. W. and Liang, P. Understanding black-box predictions via influence functions. In International conference on machine learning, pp.\ 1885--1894, 2017

    work page 2017

  25. [25]

    Learning multiple layers of features from tiny images

    Krizhevsky, A., Hinton, G., et al. Learning multiple layers of features from tiny images. 2009

    work page 2009

  26. [26]

    and Yang, X

    Le, Y. and Yang, X. Tiny imagenet visual recognition challenge. CS 231N, 7 0 (7): 0 3, 2015

    work page 2015

  27. [27]

    Gradient-based learning applied to document recognition

    LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86 0 (11): 0 2278--2324, 1998

    work page 1998

  28. [28]

    and contributors

    maintainers, T. and contributors. Torchvision: Pytorch's computer vision library. https://github.com/pytorch/vision, 2016

    work page 2016

  29. [29]

    Mehta, R., Pal, S., Singh, V., and Ravi, S. N. Deep unlearning via randomized conditionally independent hessians. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 10422--10431, 2022

    work page 2022

  30. [30]

    Descent-to-delete: Gradient-based methods for machine unlearning

    Neel, S., Roth, A., and Sharifi-Malvajerdi, S. Descent-to-delete: Gradient-based methods for machine unlearning. In Algorithmic Learning Theory, pp.\ 931--962, 2021

    work page 2021

  31. [31]

    Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. Reading digits in natural images with unsupervised feature learning. 2011

    work page 2011

  32. [32]

    A survey of machine unlearning

    Nguyen, T. T., Huynh, T. T., Nguyen, P. L., Liew, A. W.-C., Yin, H., and Nguyen, Q. V. H. A survey of machine unlearning. arXiv preprint arXiv:2209.02299, 2022

    work page arXiv 2022

  33. [33]

    Unlearning graph classifiers with limited data resources

    Pan, C., Chien, E., and Milenkovic, O. Unlearning graph classifiers with limited data resources. In Proceedings of the ACM Web Conference 2023, pp.\ 716--726, 2023

    work page 2023

  34. [34]

    Pytorch: An imperative style, high-performance deep learning library

    Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32, 2019

    work page 2019

  35. [35]

    Sekhari, A., Acharya, J., Kamath, G., and Suresh, A. T. Remember what you want to forget: Algorithms for machine unlearning. Advances in Neural Information Processing Systems, 34: 0 18075--18086, 2021

    work page 2021

  36. [36]

    Spartan: Efficient and general-purpose zksnarks without trusted setup

    Setty, S. Spartan: Efficient and general-purpose zksnarks without trusted setup. In Annual International Cryptology Conference, pp.\ 704--737, 2020

    work page 2020

  37. [37]

    Membership inference attacks are easier on difficult problems

    Shafran, A., Peleg, S., and Hoshen, Y. Membership inference attacks are easier on difficult problems. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 14820--14829, 2021

    work page 2021

  38. [38]

    Membership inference attacks against machine learning models

    Shokri, R., Stronati, M., Song, C., and Shmatikov, V. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pp.\ 3--18, 2017

    work page 2017

  39. [39]

    A., and Anderson, R

    Shumailov, I., Shumaylov, Z., Kazhdan, D., Zhao, Y., Papernot, N., Erdogdu, M. A., and Anderson, R. J. Manipulating sgd with data ordering attacks. Advances in Neural Information Processing Systems, 34: 0 18021--18032, 2021

    work page 2021

  40. [40]

    M., Song, L., Wagh, S., and Mittal, P

    Sommer, D. M., Song, L., Wagh, S., and Mittal, P. Athena: Probabilistic verification of machine unlearning. Proc. Privacy Enhancing Technol, 3: 0 268--290, 2022

    work page 2022

  41. [41]

    K., Chundawat, V

    Tarun, A. K., Chundawat, V. S., Mandal, M., and Kankanhalli, M. Deep regression unlearning. In International Conference on Machine Learning, pp.\ 33921--33939, 2023

    work page 2023

  42. [42]

    On the necessity of auditable algorithmic definitions for machine unlearning

    Thudi, A., Jia, H., Shumailov, I., and Papernot, N. On the necessity of auditable algorithmic definitions for machine unlearning. In 31st USENIX Security Symposium (USENIX Security 22), pp.\ 4007--4022, 2022

    work page 2022

  43. [43]

    A., and Arora, R

    Ullah, E., Mai, T., Rao, A., Rossi, R. A., and Arora, R. Machine unlearning via algorithmic stability. In Conference on Learning Theory, pp.\ 4126--4142, 2021

    work page 2021

  44. [44]

    Machine unlearning of features and labels

    Warnecke, A., Pirch, L., Wressnegger, C., and Rieck, K. Machine unlearning of features and labels. In Network and Distributed System Security Symposium, NDSS , 2023

    work page 2023

  45. [45]

    Proof of unlearning: Definitions and instantiation

    Weng, J., Yao, S., Du, Y., Huang, J., Weng, J., and Wang, C. Proof of unlearning: Definitions and instantiation. arXiv preprint arXiv:2210.11334, 2022

    work page arXiv 2022

  46. [46]

    Gif: A general graph unlearning strategy via influence function

    Wu, J., Yang, Y., Qian, Y., Sui, Y., Wang, X., and He, X. Gif: A general graph unlearning strategy via influence function. In Proceedings of the ACM Web Conference 2023, pp.\ 651--661, 2023 a

    work page 2023

  47. [47]

    Wu, K., Shen, J., Ning, Y., Wang, T., and Wang, W. H. Certified edge unlearning for graph neural networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.\ 2606--2617, 2023 b

    work page 2023

  48. [48]

    Xu, H., Zhu, T., Zhang, L., Zhou, W., and Yu, P. S. Machine unlearning: A survey. ACM Computing Surveys, pp.\ 1--36, 2023

    work page 2023

  49. [49]

    E., Li, G., Wang, T., and Feng, J

    Yuan, L., Tay, F. E., Li, G., Wang, T., and Feng, J. Revisiting knowledge distillation via label smoothing regularization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 3903--3911, 2020

    work page 2020

  50. [50]

    Bayesian nonparametric federated learning of neural networks

    Yurochkin, M., Agarwal, M., Ghosh, S., Greenewald, K., Hoang, N., and Khazaeni, Y. Bayesian nonparametric federated learning of neural networks. In International conference on machine learning, pp.\ 7252--7261. PMLR, 2019

    work page 2019

  51. [51]

    Towards certified unlearning for deep neural networks

    Zhang, B., Dong, Y., Wang, T., and Li, J. Towards certified unlearning for deep neural networks. In International Conference on Machine Learning, 2024

    work page 2024

  52. [52]

    Prompt certified machine unlearning with randomized gradient smoothing and quantization

    Zhang, Z., Zhou, Y., Zhao, X., Che, T., and Lyu, L. Prompt certified machine unlearning with randomized gradient smoothing and quantization. Advances in Neural Information Processing Systems, 35: 0 13433--13455, 2022

    work page 2022

  53. [53]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

    work page