Recent multimodal deepfake detection methods typically rely on single-stage training, which can cause the model to focus on dataset-specific multimodal cues while missing important features that are helpful to detect unseen manipulations, thereby limiting generalization. While some approaches attempt to address this using self-supervised audio-visual pretraining, they may not fully exploit cross-modal temporal information. Also, they often assume that manipulations affect the entire video, ignoring more realistic cases where only short segments are altered. To overcome these limitations, we propose a two-stage training framework that first learns audio-visual temporal alignment in real videos and then uses this information to detect and localize potential deepfakes by identifying temporal inconsistencies. We propose a self-supervised shift-prediction pretraining objective to fully understand cross-modal temporal alignment across multiple temporal shifts applied to the audio input. The pretrained features enable the model to identify manipulations across entire videos as well as accurately localize deepfake segments in partially tampered content. Moreover, the pretrained components do not require task-specific fine-tuning, improving the model’s flexibility for both classification and localization. Experiments on benchmark datasets demonstrate strong within-dataset performance, robust generalization to new manipulations and datasets, and accurate temporal localization.1

Reconfigurable intelligent surfaces (RISs) hold great potential for addressing the challenges in terahertz (THz) communication. However, a single RIS cannot sufficiently overcome the high path loss of THz frequencies. This paper proposes a beam routing scheme for multi-RIS-assisted sixth-generation (6G) networks, employing a dynamic programming algorithm to optimize the propagation environment through multiple RISs for efficient multi-hop communication. A closed-form expression for ergodic capacity is derived. Simulation results demonstrate significant enhancements in throughput and reliability, highlighting the advantages of this approach over traditional methods and supporting the deployment of THz networks for future wireless applications.

The recent coronavirus disease (COVID-19) has become a pandemic and has affected the entire globe. During the pandemic, we have observed a spike in cases related to mental health, such as anxiety, stress, and depression. Depression significantly influences most diseases worldwide, making it difficult to detect mental health conditions in people due to unawareness and unwillingness to consult a doctor. However, nowadays, people extensively use online social media platforms to express their emotions and thoughts. Hence, social media platforms are now becoming a large data source that can be utilized for detecting depression and mental illness. However, the existing approaches often overlook data sparsity in tweets and the multimodal aspects of social media. In this article, we propose a novel multimodal framework that combines textual, user-specific, and image analysis to detect depression among social media users. To provide enough context about the user’s emotional state, we propose the following: 1) an extrinsic feature by harnessing the URLs present in tweets and 2) extracting textual content present in images posted in tweets. We also extract five sets of features belonging to different modalities to describe a user. In addition, we introduce a deep learning model, the visual neural network (VNN), to generate embeddings of user-posted images, which are used to create the visual feature vector for prediction. We contribute a curated COVID-19 dataset of depressed and nondepressed users for research purposes and demonstrate the effectiveness of our model in detecting depression during the COVID-19 outbreak. Our model outperforms the existing state-of-the-art methods over a benchmark dataset by 2%–8% and produces promising results on the COVID-19 dataset. Our analysis highlights the impact of each modality and provides valuable insights into users’ mental and emotional states.