foolbox/attacks/carlini_wagner.py
def loss_function(cls, const, a, x, logits, reconstructed_original,
                      confidence, min_, max_):
        """Returns the loss and the gradient of the loss w.r.t. x,
        assuming that logits = model(x)."""

        targeted = a.target_class() is not None
        if targeted:
            c_minimize = cls.best_other_class(logits, a.target_class())
            c_maximize = a.target_class()
        else:
            c_minimize = a.original_class
            c_maximize = cls.best_other_class(logits, a.original_class)

        is_adv_loss = logits[c_minimize] - logits[c_maximize]

        # is_adv is True as soon as the is_adv_loss goes below 0
        # but sometimes we want additional confidence
        is_adv_loss += confidence
        is_adv_loss = max(0, is_adv_loss)

        s = max_ - min_
        squared_l2_distance = np.sum((x - reconstructed_original)**2) / s**2
        total_loss = squared_l2_distance + const * is_adv_loss

        # calculate the gradient of total_loss w.r.t. x
        logits_diff_grad = np.zeros_like(logits)
        logits_diff_grad[c_minimize] = 1
        logits_diff_grad[c_maximize] = -1
        is_adv_loss_grad = a.backward(logits_diff_grad, x)
        assert is_adv_loss >= 0
        if is_adv_loss == 0:
            is_adv_loss_grad = 0

        squared_l2_distance_grad = (2 / s**2) * (x - reconstructed_original)

        total_loss_grad = squared_l2_distance_grad + const * is_adv_loss_grad
        return total_loss, total_loss_grad
def loss_function(cls, const, a, x, logits, reconstructed_original,
                      confidence, min_, max_):
        """Returns the loss and the gradient of the loss w.r.t. x,
        assuming that logits = model(x)."""

        targeted = a.target_class() is not None
        if targeted:
            c_minimize = cls.best_other_class(logits, a.target_class())
            c_maximize = a.target_class()
        else:
            c_minimize = a.original_class
            c_maximize = cls.best_other_class(logits, a.original_class)

        is_adv_loss = logits[c_minimize] - logits[c_maximize]

        # is_adv is True as soon as the is_adv_loss goes below 0
        # but sometimes we want additional confidence
        is_adv_loss += confidence
        is_adv_loss = max(0, is_adv_loss)

        s = max_ - min_
        squared_l2_distance = np.sum((x - reconstructed_original)**2) / s**2
        total_loss = squared_l2_distance + const * is_adv_loss

        # calculate the gradient of total_loss w.r.t. x
        logits_diff_grad = np.zeros_like(logits)
        logits_diff_grad[c_minimize] = 1
        logits_diff_grad[c_maximize] = -1
        is_adv_loss_grad = a.backward(logits_diff_grad, x)
        assert is_adv_loss >= 0
        if is_adv_loss == 0:
            is_adv_loss_grad = 0

        squared_l2_distance_grad = (2 / s**2) * (x - reconstructed_original)

        total_loss_grad = squared_l2_distance_grad + const * is_adv_loss_grad
        return total_loss, total_loss_grad
def loss_function ( cls , const , a , x , logits , reconstructed_original , confidence , min_ , max_ ) : targeted = a . target_class ( ) is not None if targeted : c_minimize = cls . best_other_class ( logits , a . target_class ( ) ) c_maximize = a . target_class ( ) else : c_minimize = a . original_class c_maximize = cls . best_other_class ( logits , a . original_class ) is_adv_loss = logits [ c_minimize ] - logits [ c_maximize ] # is_adv is True as soon as the is_adv_loss goes below 0 # but sometimes we want additional confidence is_adv_loss += confidence is_adv_loss = max ( 0 , is_adv_loss ) s = max_ - min_ squared_l2_distance = np . sum ( ( x - reconstructed_original ) ** 2 ) / s ** 2 total_loss = squared_l2_distance + const * is_adv_loss # calculate the gradient of total_loss w.r.t. x logits_diff_grad = np . zeros_like ( logits ) logits_diff_grad [ c_minimize ] = 1 logits_diff_grad [ c_maximize ] = - 1 is_adv_loss_grad = a . backward ( logits_diff_grad , x ) assert is_adv_loss >= 0 if is_adv_loss == 0 : is_adv_loss_grad = 0 squared_l2_distance_grad = ( 2 / s ** 2 ) * ( x - reconstructed_original ) total_loss_grad = squared_l2_distance_grad + const * is_adv_loss_grad return total_loss , total_loss_grad
Returns the loss and the gradient of the loss w.r.t. x,
        assuming that logits = model(x).
Returns the loss and the gradient of the loss w . r . t . x assuming that logits = model ( x ) .