Bioinspired Adaptive-Depth Neural Growth for Deepfake Video Forensics: An Entropy-Guided State-Space Framework

Deepfake videos currently facilitate extensive financial deception, political misinformation, and unauthorized imagery, with anticipated U.S. losses from deepfake-related fraud surpassing $40 billion by 2027; human evaluators accurately recognize high-quality forgeries merely 25% of the time, highlighting the pressing necessity for automated, widely applicable detection mechanisms. Adaptive-depth architectures offer an intriguing alternative to fixed-depth deepfake detectors when the optimal model capacity is indeterminate in advance. This study presents the Adaptive Entropy-Guided ICA State-Space Model Forgery Detector (AEGIS-FD), a deepfake detection framework at the video level that progressively increases its depth from one to eight layers via an entropy-driven growth mechanism, attaining peak validation performance at a depth of six. The design incorporates a three-dimensional spatiotemporal stem, Sinkhorn-normalized manifold-constrained hyper-coupling (mHC) layers for balanced temporal integration, a selected state-space temporal block for sequence depiction, and FastICA-based initialization for newly introduced layers. Evaluated using Celeb-DF v2, AEGIS-FD achieves a test AUC of 0.9600 and a validation AUC of 0.9607, above the performance of a single-layer Mamba SSM baseline (AUC = 0.8355). In comparison to a fixed-depth-6 baseline, the model demonstrates consistent improvements over five random seeds (96.12 ± 0.28 vs. 94.84 ± 0.44 AUC; p = 0.0022), suggesting that adaptive development provides advantages that exceed mere depth. In a zero-shot cross-dataset evaluation—trained on Celeb-DF v2 and assessed without fine-tuning on a FaceForensics++ (FF++) C23 subset comprising 1000 original and 1000 FaceSwap videos—AEGIS-FD achieves an AUC of 89.2 compared to 88.4 for the corresponding baseline (+0.8 AUC), providing initial proof of cross-dataset transferability. These findings suggest that adaptive-depth growth presents a viable approach for detecting deepfakes at the video level, while further validation across various datasets and modification techniques is essential.