Adversarial Machine UNlearning (AMUN)

• Adversarial Machine UNlearning is a research domain focused on erasing designated training data influence while defending against adversarial threats and ensuring privacy.
• It leverages adversarial examples, min–max optimization, and proxy data generation to robustly unlearn and maintain model utility across various applications.
• Evaluation frameworks in AMUN use membership inference accuracy, cryptographic game-theory, and rigorous testing to certify effective and secure data removal.

Adversarial Machine UNlearning (AMUN) is a research domain within machine unlearning focused on the dual problem of erasing the influence of specified training data from a model—often under stringent privacy or regulatory constraints—while explicitly modeling and defending against adversarial threats that aim to subvert, poison, or circumvent the unlearning process. AMUN methods leverage adversarial examples, min–max optimization, proxied data generation, bi-level formulations, and rigorous adversarial evaluation frameworks, with extensive theoretical and empirical validation across image classifiers, generative networks, and federated learning systems.

1. Fundamental Concepts and Adversarial Threat Models

Machine unlearning aims to ensure that a trained model $F_\theta$ “forgets” the influence of a designated subset of data $\mathcal D_f$ (forget set), producing an updated model $\theta_u$ whose behavior matches retraining on the retained set $\mathcal D_r = \mathcal D \setminus \mathcal D_f$ (Ebrahimpour-Boroojeny et al., 2 Mar 2025). In AMUN, this problem is compounded by the presence of adversarial actors who may:

• Manipulate the forget set: Choosing $\mathcal D_f$ to maximally degrade utility or evade detection (worst-case adversarial selection) (Fan et al., 12 Mar 2024, Jasiorski et al., 15 Jul 2025).
• Craft adversarial unlearning requests: Submitting forged or stealthy queries that trigger catastrophic forgetting or embed backdoors (Huang et al., 12 Oct 2024, Lu et al., 21 Aug 2025).
• Exploit privacy leaks: Using inversion or membership attacks to recover forgotten data or reconstruct sensitive features (Hu et al., 4 Apr 2024, Di et al., 11 Jun 2024).

A high-level formalization from (Di et al., 11 Jun 2024) models AMUN as a Stackelberg game between a defender (unlearner) and adversarial auditor, with objectives: $$\min_{\theta_u}\;\max_{\theta_a}\;\Bigl[\ell(\mathcal D_r;\theta_u)\;+\;\lambda\,V(\tilde D^{tr}_{\theta_u};\theta_a)\Bigr],$$ where $\ell$ is the utility loss, $V$ the auditor’s attack success rate, and $\lambda$ a trade-off parameter.

2. Methodological Innovations: Adversarial Proxy Data and Min–Max Training

AMUN algorithms frequently exploit adversarial examples and min–max optimization as central mechanisms:

• Adversarial proxy generation is key in zero-shot settings, where access to retained data is unavailable. The ZS-PAG method constructs adversarial surrogates $x_{\mathrm{adv},i}$ for each forget example $(x_i, y_i)$ using projected gradient descent, selecting the second-most likely class $y_{\mathrm{target}}$ under the original model as the adversarial label (Chen et al., 29 Jul 2025):

$$\min_{||\delta||_p \le \epsilon} \mathcal L(f(x_i + \delta; \theta), y_{\mathrm{target}})$$

These proxies enable estimation of feature subspaces and orthogonal-projection unlearning, guaranteeing null-space stability on the retained loss.

• Adversarial training loop (min–max optimization) pits a defender (model) network against a strong membership inference attacker—often transformer-based—forcing the defender to scrub residual membership signals from $\mathcal D_f$ while preserving accuracy on $\mathcal D_r$ (Sharma et al., 10 Feb 2024). A self-supervised regularizer may further align feature representations between the forget and validation sets.
• Influence-based pseudo-labeling leverages classical influence functions to optimize pseudo-label assignments for unlearning proxies, minimizing detrimental impacts on remaining data (Chen et al., 29 Jul 2025).
• Adversarial mixup and boundary regularization employs synthesized mixup samples lying on the interpolation manifold between forget and retain sets, training a generator-unlearner pair in a min–max regime to prevent catastrophic boundary shifts (Peng et al., 14 Feb 2025).
• Attack-and-reset parameter masking constructs adversarial noise perturbations for the forget set, identifies filters most affected, and resets them to the original random initialization, followed by fine-tuning on retained data (Jung et al., 17 Jan 2024).

3. Evaluation Frameworks and Metrics

Rigorous evaluation of AMUN methods necessitates both statistical and adversarial criteria:

• Membership inference attack (MIA) accuracy remains the gold standard; the best AMUN methods achieve MIA accuracy near random guessing ($\approx 50\%$) on the forget set, matching retraining baselines (Ebrahimpour-Boroojeny et al., 2 Mar 2025, Ebrahimpour-Boroojeny, 7 Dec 2025).
• Cryptographic game-theoretic evaluation recasts assessment as a distinguishing game between unlearners and MIAs, defining “unlearning quality” $$Q(\text{Unlearn}) = 1 - \sup_{A} \mathrm{Adv}(A, \text{Unlearn})$$; the swap-test and provable bounds are used for efficiency and reliability (Tu et al., 17 Apr 2024).
• Worst-case forget set identification applies bi-level optimization to find subsets of the training data that are maximally resistant to erasure (hardest to unlearn), revealing vulnerabilities in existing strategies and underscoring the necessity of adversarially robust unlearning (Fan et al., 12 Mar 2024).
• Conformal prediction-inspired metrics such as CR and MIACR offer more nuanced probes of post-unlearning uncertainty and membership leakage (Shi et al., 31 Jan 2025).
• Backdoor and property generalization tests are critical to ensure adversarial risks (poison triggers, fairness gaps, robustness) are purged, not just simple memorization (Wei et al., 2023, Lu et al., 21 Aug 2025, Goel et al., 2022).

4. Applications: Federated Learning, Generative Models, and LLMs

AMUN methodologies and attacks have been adapted to multiple complex domains:

• Federated learning introduces new adversarial surfaces. The BadFU attack shows that camouflage samples can mask backdoor gradients during training but activate a powerful backdoor after their removal via federated unlearning protocols, effectively bypassing aggregation and verification schemes (Lu et al., 21 Aug 2025).
• Generative adversarial networks (GANs) necessitate substitution mechanisms and latent-space preservation during unlearning. Cascaded item/class-level unlearning, with substitute image mappings and fake-label discriminator targets, allows rapid removal (up to 200× faster than retraining) with minimal degradation (Sun et al., 2023).
• Multi-GAN architectures use synthetic data generation and adversarial label inversion to facilitate classifier unlearning, achieving near-random membership inference advantage (Hatua et al., 26 Jul 2024).
• LLMs and hazardous knowledge: Recent work demonstrates that model editing (e.g., RMU, NPO) does not robustly remove forbidden concepts, as adversarial white-box attacks (directional ablation, minimal finetuning, adversarial prefixes) can recover most “forgotten” capabilities despite post-hoc unlearning (Łucki et al., 26 Sep 2024).

5. Defensive Mechanisms and Mitigation Strategies

Robust AMUN must address both intentional and incidental adversarial risks:

• Healing via spare/twin example injection: Upon unlearning a target, fine-tuning on a “twin” (similar but unused) sample minimizes the performance gap to gold-standard retraining, offering a low-complexity defense against adversarial accuracy drops (Jasiorski et al., 15 Jul 2025).
• Verification and authentication protocols: Image hashing, perceptual distances, and exact-match checks inadequately block stealthy adversarial forget requests; cryptographic commitments and differential privacy–style auditing are suggested as more principled protections (Huang et al., 12 Oct 2024).
• Certified unlearning: Addition of calibrated noise and influence-function based removal can produce approximate differential privacy guarantees, albeit with utility–privacy trade-offs (Nguyen et al., 2022). Certified bounds are increasingly seen as necessary for adversarial robustness.
• Parameter obfuscation: Model pruning and noise injection into unlearning updates blunt inversion attacks, though at a cost in utility (Hu et al., 4 Apr 2024).
• Self-supervised feature alignment, spectral-norm clipping, and robustness regularization: These approaches stabilize unlearning boundary shifts and reduce the risk of catastrophic accuracy collapse (Ebrahimpour-Boroojeny, 7 Dec 2025).

6. Open Challenges and Future Directions

Current AMUN advances have exposed new research frontiers and unresolved obstacles:

• Adversarial resilience: Many unlearning algorithms (particularly relabeling-based or simple regularizers) can be subverted by worst-case selections; future methods must optimize against adversarial bilevel scenarios (Fan et al., 12 Mar 2024).
• Utility–privacy trade-offs: Achieving exact $\phi_u \approx \phi_r$ while maintaining utility and computational efficiency remains elusive, especially in streaming, federated, or high-dimensional settings (Nguyen et al., 2022, Ebrahimpour-Boroojeny, 7 Dec 2025).
• Evaluation suites and benchmarks: Unified adversarial benchmarks spanning image, generative, and language tasks are necessary to compare AMUN performance (Nguyen et al., 2022).
• Theoretical guarantees: Extensive formal analysis is required to provide certified privacy bounds, composability under sequential requests, and interpretable explanations of feature and parameter scrubbing (Tu et al., 17 Apr 2024, Shi et al., 31 Jan 2025).
• Scalability and automation: Efficient algorithms for multi-modal unlearning (e.g., text, graph, tabular), automated selection of healing/twin sets, and streaming protocols are emerging directions.

Adversarial Machine UNlearning is thus defined by its emphasis on adversarially robust, privacy-preserving, and utility-stable removal of training influence, under the scrutiny of both rigorous evaluation and hostile adversarial attack. The field is characterized by rapid algorithmic innovation and growing recognition of adversarial threat modeling as fundamental to practical deployment.