def _color_projection(  # pylint: disable=R0201
        self, tile: "torch.Tensor", x_ref: "torch.Tensor", epsilon: float
    ) -> "torch.Tensor":
        """
        Convert statistics information from target to source.

        :param tile: The target to convert.
        :param x_ref: The source data.
        :param epsilon: The radius of the epsilon bass.
        :return: The converted tile.
        """
        import torch

        if len(tile.shape) == 3:
            tile = tile[None, :]
        if len(x_ref.shape) == 3:
            x_ref = x_ref[None, :]

        pert = tile - x_ref
        cond = torch.abs(pert) > epsilon
        sign = (torch.rand_like(pert) - 0.5) * 2

        u_bound = torch.max(pert, torch.ones_like(pert) * epsilon)
        l_bound = torch.min(pert, torch.ones_like(pert) * -epsilon)
        set1 = torch.where(sign > 0, 0.5 * pert, pert - torch.sign(pert) * epsilon)
        set1 = torch.clamp(set1, l_bound, u_bound)
        set1 = set1 + x_ref

        set2 = tile
        mean_s = torch.mean(x_ref, dim=(-2, -1), keepdim=True)
        mean_t = torch.mean(x_ref, dim=(-2, -1), keepdim=True)
        std_s = torch.std(set2, dim=(-2, -1), keepdim=True)
        std_t = torch.std(set2, dim=(-2, -1), keepdim=True)
        scale = std_s / std_t
        set2 = (set2 - mean_t) * scale + mean_s
        set2 = torch.clamp(set2, 0.0, 1.0)

        set2 = set2 + sign * epsilon * scale
        set2 = torch.clamp(set2, 0, 1)

        updated = torch.where(cond, set1, set2)

        return updated

        Convert statistics information from target to source.

        :param tile: The target to convert.
        :param x_ref: The source data.
        :param epsilon: The radius of the epsilon bass.
        :return: The converted tile.
        
art/attacks/evasion/steal_now_attack_later/steal_now_attack_later.py