Fused Swish-ReLU Efficient-Net Model for Deepfakes Detection
Hafsa Ilyas, Ali Javed, Muteb Aljasem, Mustafa Alhababi · 2023
With the rapid development of sophisticated deepfakes generation methods, the realism of fake content has reached the level where it becomes difficult for human eyes to identify such high-quality fake images/videos, thus increasing the demand for developing deepfakes detection methods. The diversity in deepfakes images/videos in terms of ethnicity, illumination condition, skin tone, age, background setting, and generation algorithms makes the detection task quite difficult. To better address the aforementioned challenges, we present a novel Swish-ReLU Efficient-Net (SRE-Net) that is robust to the identification of deepfakes generated using different face-swap and face-reenactment techniques. More precisely, we fused two EfficienNet-b0 models, one with the ReLU and the other with the Swish activation function along with layer freezing to achieve better detection results. Our SRE-Net attained the average accuracy and precision of 96.5% and 97.07% on the FaceForensics++ dataset, and 88.41% and 91.28% on the DFDC-preview dataset. The high detection results demonstrate the effectiveness of SRE-Net while detecting the deepfakes generated using different manipulation algorithms.