You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Let f(\bm{x}) be an affine classifier, i.e., f(\bm{x})=\mathbf{W}^{\top}\bm{x}+\bm{b} for a given \mathbf{W} and \bm{b}. Since the mapping \hat{k} is the outcome of a one-vs-all classification scheme, the minimal perturbation to fool the classifier can be rewritten as follows\begin{split}&\operatorname*{arg\,min}_{\bm{r}}\|\bm{r}\|_{2}\\&\text{s.t. }\exists k:\bm{w}_{k}^{\top}(\bm{x}_{0}+\bm{r})+b_{k}\geq\bm{w}_{\hat{k}(\bm{x}_{0})}^{\top}(\bm{x}_{0}+\bm{r})+b_{\hat{k}(\bm{x}_{0})},\end{split}(6)where \bm{w}_{k} is the k^{\text{th}} column of \mathbf{W}. Geometrically, the above problem corresponds to the computation of the distance between \bm{x}_{0} and the complement of the convex polyhedron P,\displaystyle P=\bigcap_{k=1}^{c}\{\bm{x}:f_{\hat{k}(\bm{x}_{0})}(\bm{x})\geq f_{k}(\bm{x})\},(7)where \bm{x}_{0} is located inside P.We denote this distance by \text{{dist}}(\bm{x}_{0},P^{c}).The polyhedron P defines the region of the space where f outputs the label \hat{k}(\bm{x}_{0}). This setting is depicted in Figure 4. The solution to the problem in Eq. (6) can be computed in closed form as follows. Define \hat{l}(\bm{x}_{0}) to be the closest hyperplane of the boundary of P (e.g. \hat{l}(\bm{x}_{0})=3 in Figure 4). Formally, \hat{l}(\bm{x}_{0}) can be computed as follows\hat{l}(\bm{x}_{0})=\operatorname*{arg\,min}_{k\neq{\hat{k}(\bm{x}_{0})}}\frac{\left|f_{k}(\bm{x}_{0})-f_{\hat{k}(\bm{x}_{0})}(\bm{x}_{0})\right|}{\|\bm{w}_{k}-\bm{w}_{\hat{k}(\bm{x}_{0})}\|_{2}}.(8)The minimum perturbation \bm{r}_{*}(\bm{x}_{0}) is the vector that projects \bm{x}_{0} on the hyperplane indexed by \hat{l}(\bm{x}_{0}), i.e.,\bm{r}_{*}(\bm{x}_{0})=\frac{\left|f_{\hat{l}(\bm{x}_{0})}(\bm{x}_{0})-f_{\hat{k}(\bm{x}_{0})}(\bm{x}_{0})\right|}{\|\bm{w}_{\hat{l}(\bm{x}_{0})}-\bm{w}_{\hat{k}(\bm{x}_{0})}\|_{2}^{2}}(\bm{w}_{\hat{l}(\bm{x}_{0})}-\bm{w}_{\hat{k}(\bm{x}_{0})}).(9)In other words, we find the closest projection of \bm{x}_{0} on faces of P. As a multiclass classifier can be viewed as aggregation of binary classifiers, we first propose the algorithm for binary classifiers.That is, we assume here \hat{k}(\bm{x})=\text{sign}(f(\bm{x})), where f is an arbitrary scalar-valued image classification function f:\mathbb{R}^{n}\rightarrow\mathbb{R}. We also denote by \mathscr{F}\triangleq\{\bm{x}:f(\bm{x})=0\} the level set at zero of f.We begin by analyzing the case where f is an affine classifier f(\bm{x})=\bm{w}^{T}\bm{x}+b, and then derive the general algorithm, which can be applied to any differentiable binary classifier f.

Question: What does an "affine classifier" mean?
The affine classifier is the classifier in the form of an affine function. The general form that is used in the paper is the function f: R^n -> R^m, where f(x) = W^T * x + B, for the given matrix and vector W and B.