deepfool.py 文件源码

def _deepfoolx(model, x, epochs, eta, clip_min, clip_max, min_prob):
    y0 = tf.stop_gradient(model(x))
    y0 = tf.reshape(y0, [-1])
    k0 = tf.argmax(y0)

    ydim = y0.get_shape().as_list()[0]
    xdim = x.get_shape().as_list()[1:]
    xflat = _prod(xdim)

    def _cond(i, z):
        xadv = tf.clip_by_value(x + z*(1+eta), clip_min, clip_max)
        y = tf.reshape(model(xadv), [-1])
        p = tf.reduce_max(y)
        k = tf.argmax(y)
        return tf.logical_and(tf.less(i, epochs),
                              tf.logical_or(tf.equal(k0, k),
                                            tf.less(p, min_prob)))

    def _body(i, z):
        xadv = tf.clip_by_value(x + z*(1+eta), clip_min, clip_max)
        y = tf.reshape(model(xadv), [-1])

        gs = [tf.reshape(tf.gradients(y[i], xadv)[0], [-1])
              for i in range(ydim)]
        g = tf.stack(gs, axis=0)

        yk, yo = y[k0], tf.concat((y[:k0], y[(k0+1):]), axis=0)
        gk, go = g[k0], tf.concat((g[:k0], g[(k0+1):]), axis=0)

        yo.set_shape(ydim - 1)
        go.set_shape([ydim - 1, xflat])

        a = tf.abs(yo - yk)
        b = go - gk
        c = tf.norm(b, axis=1)
        score = a / c
        ind = tf.argmin(score)

        si, bi = score[ind], b[ind]
        dx = si * bi
        dx = tf.reshape(dx, [-1] + xdim)
        return i+1, z+dx

    _, noise = tf.while_loop(_cond, _body, [0, tf.zeros_like(x)],
                             name='_deepfoolx_impl', back_prop=False)
    return noise