Lemma A.2. Algorithm ϕ gives a lower bound on the query γ . That is, ϕ (x, R) γ (x, R).

Achieving such guarantees generally requires the injection of noise, either directly into parameter estimates or into the estimation process.

MF mechanism consistently performs at least as well as the best prior methods.

These counts have very high global sensitivity.

The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations.

Fan et al. [2021] incorporates high-frequency views into contrastive learning, leading to the transfer However, there are also several works that challenge the validity of this assumption. Yin et al. [2019] proposes a robustness analysis strategy based on Fourier Heatmaps, which utilizes a model's sensitivity to frequency-bases. Maiya et al. [2021] believes that model robustness does not have an intrinsic connection In addition to the perspective on frequency components, Chen et al. [2021] has shown that the CNN model should be consistent with the Human Visual System, with To show the power law distribution of natural images, we select CIFAR-10 Krizhevsky et al. [2009], Tiny-ImageNet Le and Y ang [2015] and ImageNet Deng et al. [2009] to conduct experiments. We show an example of division on ImageNet, as shown in Fig.2, in which the high-and low-frequency components of the image obtained according to the division radius are also in line with our We conduct experiments on naturally trained models. We conduct experiments on test set of CIFAR10, Tiny-ImageNet, ImageNet-1k datasets.