  The rapid advancement of deepfake technologies raises significant concerns
about the security of face recognition systems. While existing methods leverage
the clues left by deepfake techniques for face forgery detection, malicious
users may intentionally manipulate forged faces to obscure the traces of
deepfake clues and thereby deceive detection tools. Meanwhile, attaining
cross-domain robustness for data-based methods poses a challenge due to
potential gaps in the training data, which may not encompass samples from all
relevant domains. Therefore, in this paper, we introduce a solution - a
Cross-Domain Robust Bias Expansion Network (BENet) - designed to enhance face
forgery detection. BENet employs an auto-encoder to reconstruct input faces,
maintaining the invariance of real faces while selectively enhancing the
difference between reconstructed fake faces and their original counterparts.
This enhanced bias forms a robust foundation upon which dependable forgery
detection can be built. To optimize the reconstruction results in BENet, we
employ a bias expansion loss infused with contrastive concepts to attain the
aforementioned objective. In addition, to further heighten the amplification of
forged clues, BENet incorporates a Latent-Space Attention (LSA) module. This
LSA module effectively captures variances in latent features between the
auto-encoder's encoder and decoder, placing emphasis on inconsistent
forgery-related information. Furthermore, BENet incorporates a cross-domain
detector with a threshold to determine whether the sample belongs to a known
distribution. The correction of classification results through the cross-domain
detector enables BENet to defend against unknown deepfake attacks from
cross-domain. Extensive experiments demonstrate the superiority of BENet
compared with state-of-the-art methods in intra-database and cross-database
evaluations.
