Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning

TL;DR

This paper introduces an adversarial learning approach to extract intrinsic discriminative features for face forgery detection, improving generalization by removing irrelevant factors like forgery methods and identities.

Contribution

It proposes a novel adversarial framework with an identity discriminator to enhance the learning of intrinsic features for face forgery detection.

Findings

Improved detection accuracy in intra-dataset tests.

Enhanced cross-dataset generalization performance.

Effective elimination of identity and forgery method biases.

Abstract

Existing face forgery detection methods usually treat face forgery detection as a binary classification problem and adopt deep convolution neural networks to learn discriminative features. The ideal discriminative features should be only related to the real/fake labels of facial images. However, we observe that the features learned by vanilla classification networks are correlated to unnecessary properties, such as forgery methods and facial identities. Such phenomenon would limit forgery detection performance especially for the generalization ability. Motivated by this, we propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities, which helps classification network to learn intrinsic common discriminative features for face forgery detection. To leverage data lacking ground truth label of facial identities, we design a special identity discriminator based on similarity information derived from off-the-shelf face recognition model. With the help of adversarial learning, our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities. Extensive experiments demonstrate the effectiveness of the proposed method under both intra-dataset and cross-dataset evaluation settings.