RADNN__Robust_to_adversarial_attacks_Deep_NN (7).pdf (948.29 kB)
RADNN: ROBUST TO IMPERCEPTIBLE ADVERSARIAL ATTACKS DEEP NEURAL NETWORK
This paper presents the RADNN algorithm. The RADNN is a robust to imperceptible adversarial attack algorithm that uses the concept of data density and similarities to detect attacks on real-time. Differently from traditional deep learnings that need be trained on the attacks to be able to detect, RADNN has a mechanism that detects data patterns changes. In order to evaluate the proposed method, we considered the PerC attacks and a 1000 images from the Imagenet dataset. The RADNN could correctly identify 97.2% of the attacks.
History
Email Address of Submitting Author
e.almeidasoares@lancaster.ac.ukORCID of Submitting Author
0000-0002-2634-8270Submitting Author's Institution
Lancaster UniversitySubmitting Author's Country
- United Kingdom