We study the statistical resilience of high-dimensional data. Our results provide estimates as to the effects of adversarial noise over the anti-concentration properties of the quadratic Radamecher chaos $\boldsymbol{\xi}^{\mathsf{T}} M \boldsymbol{\xi}$, where $M$ is a fixed (high-dimensional) matrix and $\boldsymbol{\xi}$ is a conformal Rademacher vector. Specifically, we pursue the question of how many adversarial sign-flips can $\boldsymbol{\xi}$ sustain without "inflating" $\sup_{x\in \mathbb{R}} \mathbb{P} \left\{\boldsymbol{\xi}^{\mathsf{T}} M \boldsymbol{\xi} = x\right\}$ and thus "de-smooth" the original distribution resulting in a more "grainy" and adversarially biased distribution. Our results provide lower bound estimations for the statistical resilience of the quadratic and bilinear Rademacher chaos; these are shown to be asymptotically tight across key regimes.

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