Pin-Yu Chen, Huan Zhang, Yash Sharma, Jinfeng Yi, Cho-Jui Hsieh. ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models. AISec@CCS, 2017.

Chen et al. propose a gradient-based black-box attack to compute adversarial examples. Specifically, they follow the general idea of [1] where the following objective is optimized:

$\min_x \|x – x_0\|_2 + c \max\{\max_{i\neq t}\{z_i\} – z_t, - \kappa\}$.

Here, $x$ is the adversarial example based on training sample $x_0$. The second part expresses that $x$ is supposed to be misclassified, i.e. the logit $z_i$ for some $i \neq t$ distinct form the true label $t$ is supposed to be larger that the logit $z_t$ corresponding to the true label. This is optimized subject to the constraint that $x$ is a valid image.

The attack proposed in [1] assumes a white-box setting were we have access to the logits and the gradients (basically requiring access to the full model). Chen et al., in contrast want to design a black-box attacks. Therefore, they make the following changes:

  • Instead of using logits $z_i$, the probability distribution $f_i$ (i.e. the actual output of the network) is used.
  • Gradients are approximated by finite differences.

Personally, I find that the first point does violate a strict black-box setting. As company, for example, I would prefer not to give away the full probability distribution but just the final decision (or the decision plus a confidence score). Then, however, the proposed method is not applicable anymore. Anyway, the changed objective looks as follows:

$\min_x \|x – x_0\|_2 + c \max\{\max_{i\neq t}\{\log f_i\} – \log f_t, - \kappa\}$

where, according to the authors, the logarithm is essential for optimization. One remaining problem is efficient optimization with finite differences. To this end, they propose a randomized/stochastic coordinate descent algorithm. In particular, in each step, a ranodm pixel is chosen and a local update is performed by calculating the gradient on this pixel using finite differences and performing an ADAM step.

  • [1] N. Carlini, D. Wagner. Towards evaluating the robustness of neural networks. IEEE Symposium of Security and Privacy, 2017.
Also find this summary on ShortScience.org.
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