Abstract

Digital face manipulation has become a concern in the last few years due to its harmful impacts on society. It is especially concerning for high-profile celebrities because their identities can be easily manipulated using mobile or web applications such as FaceSwap and FaceApp. These manipulated faces are so close to real ones that it becomes really hard to detect them, even with bare eyes. Though deep learning-based models are predominantly used by researchers, they hardly check for the presence of irrelevant or redundant features produced by those models. To this end, we have proposed a hierarchical feature selection (HFS)-based method to detect deepfake images or videos. First, we have extracted both handcrafted and deep learning features from the inputs. Next, the HFS is applied to select a near optimal set of features. In each stage of it, a hybrid feature selection method is employed that integrates a population-based meta-heuristic model, called Grey Wolf Optimization, and a single solution-based meta-heuristic model, called the Vortex Search algorithm. We have evaluated our model on three publicly available datasets, namely Celeb-DF (V2), FaceForensics++, and Deepfake Detection Challenge (DFDC). The model provides 99.35%, 99.16%, and 85.67% AUC scores on the Celeb-DF (V2), FaceForensics++, and DFDC datasets, respectively, while using only 11.50%, 12.65%, and 10.22% of actual features. Besides, our model outperforms most of the state-of-the-art methods found in our literature review and evaluated on these three datasets.