Margin based adversarial computer program

1. A system, comprising:

a memory that stores computer executable components;

a processor, operably coupled to the memory, and that executes the computer executable components stored in the memory, wherein the computer executable components comprise:

an adversarial component that computes a perturbation that causes misclassification by a neural network classifier;

a restoration component that determines a normal vector to a constraint contour developed by the neural network classifier; and

a projection component that determines a tangential vector to the constraint contour.

2. The system of claim 1 , wherein the constraint contour separates input features from a first class and input features from a second class within a hyperplane, and wherein the adversarial component generates a convergence pathway through the hyperplane from an initial input to a nearest point on the constraint contour from an original input.

3. The system of claim 2 , wherein the adversarial component generates the convergence pathway to solve an optimization algorithm and compute the perturbation.

4. The system of claim 2 , wherein the convergence pathway comprises an iteration of the normal vector followed by the tangential vector.

5. The system of claim 2 , wherein the convergence pathway comprises a plurality of iterations of the normal vector followed by the tangential vector.

6. The system of claim 5 , wherein the restoration component re-determines the normal vector between iterations from the plurality of iterations, and wherein the projection component re-determines the tangential vector between the iterations from the plurality of iterations.

7. The system of claim 2 , wherein the adversarial component incorporates a box constraint when generating the convergence pathway such that the nearest point is an invariant point on the normal vector.

8. The system of claim 7 , wherein the initial input is generated by an initialization process selected from a group consisting of a deterministic input generation process and a random input generation process.

9. The system of claim 1 , wherein the computer executable components further comprise:

a defense component that trains the neural network classifier using the perturbation.

10. A computer-implemented method, comprising:

computing, by a system operatively coupled to a processor, a perturbation that causes misclassification by a neural network classifier;

determining, by the system, a normal vector to a constraint contour developed by the neural network classifier; and

determining, by the system, a tangential vector to the constraint contour.

11. The computer-implemented method of claim 10 , further comprising:

generating, by the system, a convergence pathway through a hyperplane from an initial input to a nearest point on the constraint contour from an original input, wherein the constraint contour separates input features from a first class and input features from a second class within the hyperplane.

12. The computer-implemented method of claim 11 , wherein the convergence pathway comprises a plurality of iterations of the normal vector followed by the tangential vector.

13. The computer-implemented method of claim 12 , further comprising:

re-determining, by the system, the normal vector between iterations from the plurality of iterations; and

re-determining, by the system, the tangential vector between the iterations from the plurality of iterations.

14. The computer-implemented method of claim 13 , wherein the generating the convergence pathway solves is performed in accordance with an optimization algorithm to facilitate the computing the perturbation.

15. The computer-implemented method of claim 13 , further comprising:

training, by the system, the neural network classifier using the perturbation.

16. A computer program product for computing a perturbation that causes misclassification by a neural network classifier, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

generate, by the processor, a convergence pathway through a hyperplane from an initial input to a point on a constraint contour, wherein the hyperplane is developed by the neural network classifier, and wherein the convergence pathway comprises a normal vector to the constraint contour and a tangential vector to the constraint contour.

17. The computer program product of claim 16 , wherein the normal vector extends from the initial input towards the constraint contour to an intermediate point, and wherein the tangential vector extends from the intermediate point along a tangent plane to the constraint contour and towards the point, and wherein the point is a nearest point on the constraint contour from an original input.

18. The computer program product of claim 17 , wherein the program instructions cause the processor to:

generate, by the processor, the initial input by an initialization process selected from a group consisting of a deterministic input generation process and a random input generation process.

19. The computer program product of claim 18 , wherein the program instructions cause the processor to:

train, by the processor, the neural network classifier using the perturbation.

20. The computer program product of claim 19 , wherein generation of the convergence pathway is in a cloud computing environment.