Reducing information dependency does not cause training data privacy. Adversarially non-robust features do
In this paper, we challenge the prevailing view that information dependency (including rote memorization) drives training data exposure to image reconstruction attacks. We show that extensive exposure can persist without rote memorization and is instead caused by a tunable connection to adversarial robustness. We begin by presenting three surprising results: (1) recent defenses that inhibit reconstruction by Model Inversion Attacks (MIAs), which evaluate leakage under an idealized attacker, do not reduce standard measures of information dependency (HSIC); (2) models that maximally memorize their training datasets remain robust to MIA reconstruction; and (3) models trained without seeing 9...
| Authors | Rasmus Torp, Shailen K. Smith, Adam Breuer |
|---|---|
| Primary category | cs.LG |
| Published | 14 July 2026 |
| Last updated at source | 7 August 2026 |
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