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

Machine unlearning in Vertical Federated Learning (VFL)has attracted growing interest, yet existing methods certify forgettingsolely using output-level metrics. We challenge these works by intro-ducing Mirage, a representation-level auditing framework built fromfour complementary diagnostics. Mirage combines linear probe recovery(LPR), centered kernel alignment (CKA), feature separability scoring,and layer-wise recovery analysis to assess what a representation actuallyretains. Extensive experiments across seven datasets and seven baselinemethods following recent VFL unlearning protocols reveal three key find-ings. (1) Forgetting gap: methods that pass output-level certification stillretain substantial class structure in their representations, with LPR ex-ceeding the retrained baseline by up to 15.4 points. CKA shows thatthese models remain structurally closer to the original than to the re-trained reference, and separability scores indicate persistent geometricdiscrimination. (2) Unlearning trilemma: no existing method simultane-ously achieves high utility, output-level forgetting, and representation-level forgetting. (3) Class-sample asymmetry: class-level forgetting leavesstrong representational traces (LPR exceeding 96% on several datasets),whereas sample-level forgetting is indistinguishable from chance (LPR≈ 50%). Layer-wise analysis further shows that residual class informationpersists across network depths. These findings call for representation-aware evaluation standards in federated unlearning research. Code ispublicly available at https://github.com/YuZhenyuLindy/Mirage.