Deep learning techniques have made it much easier to generate realistic fake content by superimposing or replacing existing images, videos, or audio with highly realistic alternative content. These manipulations often involve the faces or voices of individuals, creating convincing but fabricated representations. Due to the potential misuse of deepfakes for malicious purposes including spreading misinformation, creating fraudulent content, stealing people’s identity, and manipulating public opinion, the development of detection techniques and policies to mitigate harmful effects of deepfakes has become an important research area. In this paper, we combine both spatial and wavelet features to develop a simple yet effective model to detect deepfakes. Specifically, we pass the input color image through the first convolutional layer and employ a one-level wavelet transform to decompose the channel-wise sum of each batch of features. We then thresh-hold the subbands and reconstruct a single channel feature map, which is concatenated with the original batch of features and passed onto the subsequent layers to capture the facial manipulations using both spatial and frequency features. The expanded wavelet transformed features are fed into the VGG19 backbone to help detect deepfakes with an improved detection performance. We perform both within and cross domain evaluations to compare the performance of the proposed model and state-of-the-art peer models in terms of Area Under Curve (AUC) and Equal Error Rate (EER) metrics. Our extensive experimental results demonstrate that the proposed wavelet-integrated VGG19 model offers a more robust solution than the peer wavelet-integrated Xception model and both VGG19 and Xception baseline models to combating the proliferation of fake multimedia content on digital platforms.

Deep learning techniques have made it much easier to generate realistic fake content by superimposing or replacing existing images, videos, or audio with highly realistic alternative content. Due to the potential misuse of deepfakes for malicious purposes, the development of detection techniques and policies to mitigate harmful effects of deepfakes has become an important research area. In this paper, we combine both spatial and multi-resolution wavelet features to develop a simple yet effective model to detect deepfakes. We perform cross domain evaluations to compare the performance of the proposed model and state-ofthe-art peer models in terms of Area Under Curve (AUC) and Equal Error Rate (EER) metrics. Our extensive experimental results demonstrate that the proposed multi-resolution VGG19 model offers a more robust solution than other compared models to combating the proliferation of fake multimedia content on digital platforms.

The access to large-scale public databases along with the fast progress of deep learning techniques have led to the generation of very realistic fake content. This has raised significant concerns because of their use in social media and the generation of fake news. Thus, the detection of such manipulations has become an increasingly important research area, especially the detection of fake faces has become very important in the field of digital forensics. This paper presents a complementary attention-based deep learning system to detect fake faces. This system effectively incorporates our proposed simple Layer-Integrated Channel Attention (LICA) and Scaled Spatial Attention (SSA) mechanisms in VGG network architecture to capture the importance along each channel and at each spatial location to distinguish between real and manipulated faces and improve detection performance. Our extensive experimental results demonstrate that the proposed system outperforms the state-of-the-art system in detecting fake faces generated by each of the four commonly used manipulations including entire face synthesis, identity swap, attribute manipulation, and expression swap in terms of both accuracy and Area Under Curve (AUC) metrics. It also achieves better performance than the state-of-the-art system to detect fake faces generated by any of the four aforementioned manipulations.