A Robust Open-Set Multi-Instance Learning for Defending Adversarial Attacks in Digital Image

Kutub Uddin

Abstract:

In recent times, digital image forensics is gaining increased attention in multimedia forensics owing to the
widespread scam alertness. Several forensic methods have been
studied to establish the integrity of digital images by disclosing manipulation fingerprints. Anti-forensic (AF) attacks on
manipulated images, particularly deep learning-based adversarial
attacks using generative adversarial network (GAN), have been
successfully applied to delude forensic methods. Consequently,
an efficacious, efficient, and robust counter-AF (CAF) method
is required to secure the integrity of digital images. In this
study, we propose a robust open-set multi-instance learning
approach for exposing GAN-based AF on manipulated images by
introducing additional GAN-based operations. First, we generate
multiple real instances from real images using multiple additional
generators. Then we train an embedding network collaboratively
with multiple real instances in an open-set fashion. During
training, the embedding network learns only real images and has
no prior knowledge regarding AF images. In the testing phase,
real and AF images are processed for detection. The proposed
open-set CAF method can effectively detect AF images and is
more robust against transferable updating.