Deep neural network hardening framework

A computer program product comprising a computer readable storage medium having a computer readable program stored therein, wherein the computer readable program, when executed on a data processing system, causes the data processing system to:

• configure the data processing system to implement a hardened neural network engine that operates on a neural network to harden the neural network against evasion attacks and generates a hardened neural network;
• generate a reference training data set based on an original training data set;
• process, by the neural network, the original training data set and the reference training data set to generate first and second output data sets;
• calculate, by the hardened neural network engine, a modified loss function of the neural network, wherein the modified loss function is a combination of an original loss function associated with the neural network, and a delta function of a ratio of a similarity between the original training data set and the reference data set and a similarity between the first output data set and the second output data set;
• and
• train, by the hardened neural network engine, the neural network based on the modified loss function to generate the hardened neural network, wherein the delta function, Delta, is as follows:
• Delta(X,X′,Y,Y′)=cos(X,X′)/cos(Y,Y′),
• wherein X is the original training data set, X′ is the reference training data set, Y is the first output data set, Y′ is the second output data set, and cos is a cosine similarity function.

A data processing system comprising:

• at least one processor;
• and
• at least one memory coupled to the at least one processor, wherein the at least one memory comprises instructions which, when executed by the at least one processor, cause the at least one processor to:
• configure the data processing system to implement a hardened neural network engine that operates on a neural network to harden the neural network against evasion attacks and generates a hardened neural network;
• generate a reference training data set based on an original training data set;
• process, by the neural network, the original training data set and the reference training data set to generate first and second output data sets;
• calculate, by the hardened neural network engine, a modified loss function of the neural network, wherein the modified loss function is a combination of an original loss function associated with the neural network, and a delta function of t a ratio of a similarity between the original training data set and the reference data set and a similarity between the first output data set and the second output data set;
• and
• train, by the hardened neural network engine, the neural network based on the modified loss function to generate the hardened neural network, wherein the delta function, Delta, is as follows:
• Delta(X,X′,Y,Y′)=cos(X,X′)/cos(Y,Y′),
• wherein X is the original training data set, X′ is the reference training data set, Y is the first output data set, Y′ is the second output data set, and cos is a cosine similarity function.