  The widespread application of deep neural network (DNN) techniques is being
challenged by adversarial examples, the legitimate input added with
imperceptible and well-designed perturbations that can fool DNNs easily in the
DNN testing/deploying stage. Previous adversarial example generation algorithms
for adversarial white-box attacks used Jacobian gradient information to add
perturbations. This information is too imprecise and inexplicit, which will
cause unnecessary perturbations when generating adversarial examples. This
paper aims to address this issue. We first propose to apply a more informative
and distilled gradient information, namely integrated gradient, to generate
adversarial examples. To further make the perturbations more imperceptible, we
propose to employ the restriction combination of $L_0$ and $L_1/L_2$ secondly,
which can restrict the total perturbations and perturbation points
simultaneously. Meanwhile, to address the non-differentiable problem of $L_1$,
we explore a proximal operation of $L_1$ thirdly. Based on these three works,
we propose two Integrated gradient based White-box Adversarial example
generation algorithms (IWA): IFPA and IUA. IFPA is suitable for situations
where there are a determined number of points to be perturbed. IUA is suitable
for situations where no perturbation point number is preset in order to obtain
more adversarial examples. We verify the effectiveness of the proposed
algorithms on both structured and unstructured datasets, and we compare them
with five baseline generation algorithms. The results show that our proposed
algorithms do craft adversarial examples with more imperceptible perturbations
and satisfactory crafting rate. $L_2$ restriction is more suitable for
unstructured dataset and $L_1$ restriction performs better in structured
dataset.
