The rapid advancement in deepfake technology has enabled the creation of highly realistic fake images and videos, posing significant risks, especially in the context of explicit content. Such content, which often involves the alteration of an individual’s identity in sexually explicit material, can lead to defamation, harassment, and blackmail. This paper focuses on the detection of deepfakes in explicit content using a state-of-the-art ID-unaware Binary Classification method. We evaluate its effectiveness in real-world scenarios by analyzing three versions of the model with different backbones: ResNet34, EfficientNet-B3, and EfficientNet-B4. To facilitate this evaluation, we curated a dataset of 200 videos, consisting of 100 genuine videos and their corresponding deepfake counterparts, ensuring a direct comparison between genuine and altered content. Our analysis revealed a significant decrease in detection performance when applying the state-of-the-art method to explicit content. Specifically, the AUC score dropped from 93% on standard datasets such as FaceForensics++ to 62% on our explicit content dataset. Additionally, the accuracy for detecting deepfakes plummeted to around 25%, while the accuracy for genuine videos remained high at approximately 90%. We identified specific factors contributing to this decline, including unconventional makeup, lighting issues, and facial blurring due to camera distance. These findings underscore the challenges and the necessity for robust detection methods to address the unique problems posed by explicit content deepfakes, ultimately aiming to protect individuals from the potential harms associated with this technology.

Droplet transmission is one of the leading causes of the spread of respiratory infections, such as coronavirus disease (COVID-19). The proper use of face masks is an effective way to prevent the transmission of such diseases. Nonetheless, different types of masks provide various degrees of protection. Hence, automatic recognition of face mask types may benefit the control access to facilities where a specific protection degree is required. In the last two years, several deep learning models have been proposed for face mask detection and properly wearing mask recognition. However, the current publicly available datasets do not consider the different mask types and occasionally lack real-world elements needed to train robust models. In this paper, we introduce a new dataset named TFM with sufficient size and variety to train and evaluate deep learning models for face mask detection and recognition. This dataset contains more than 135,000 annotated faces from about 100,000 photographs taken in the wild. We consider four mask types (cloth, respirators, surgical and valved) as well as unmasked faces, of which up to six can appear in a single image. The photographs were mined from Twitter within two years since the beginning of the COVID-19 pandemic. Thus, they include diverse scenes with real-world variations in background and illumination. With our dataset, the performance of four state-of-the-art object detection models is evaluated. The experimental results show that YOLOv5 can achieve about 90% of [email protected], demonstrating that the TFM dataset can be used to train robust models and may help the community step forward in detecting and recognizing masked faces in the wild. Our dataset and pre-trained models used in the evaluation will be available upon the publication of this paper.

In the current pandemic of coronavirus disease (COVID-19), an effective way to prevent the transmission and infection of the virus is the proper use of face masks. However, the different types of masks provide different degrees of protection. For instance, valved masks protect the user but do not help to stop the transmission. Hence, the automatic recognition of face mask types may benefit applications that control access to facilities where a certain facepiece is required. In this paper, we propose a Twitter mining framework to gather a large-scale dataset of masked faces suitable to train deep learning-based models for face mask recognition. We employ a keyword-based selection where non-face images are discarded by an efficient face detector (Retinaface). Finally, we train a state-of-the-art CNN architecture (ConvNeXt) for recognizing the wearing mask. We also present a brief analysis of more than two million image-based tweets acquired over two years since the beginning of the pandemic. The code of the proposed framework and a preliminary dataset of more than 10K faces (manually annotated into unmasked, surgical, cloth, respirators, and valved masks) are available on github.com/GibranBenitez/FaceMask Twitter.