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Recent advances in generative visual content have led to a quantum leap in the quality of artificially generated Deepfake content. Especially, diffusion models are causing growing concerns among communities due to their ever-increasing realism. However, quantifying the realism of generated content is still challenging. Existing evaluation metrics, such as Inception Score and Fréchet inception distance, fall short on benchmarking diffusion models due to the versatility of the generated images.

Surveillance videos taken in unconstrained environments can be tampered with due to different environmental factors and malicious human activities. They often blur the video content and introduce difficulty in identifying the events in the scene. The problem is particularly acute for smart surveillance systems that need to make real-time decisions based on the video. Automatic detection of the blur anomalies in the video is crucial to these systems. In this research, a learning-based approach for camera blur detection is proposed.

AHT2D dataset is composed of Handwritten Arabic letters with diacritics. In this dataset, we have 28 letter classes according to the number of Arabic letters. Each class contains a multiple letter form. We have different letter images from different sources such as the internet, our writers, etc. The AHT2D dataset includes only isolated letters. In addition, this dataset contains different writing styles, orientations, colors, thicknesses, sizes, and backgrounds, which makes it a very large and rich dataset.

WITH the advancement in sensor technology, huge amounts of data are being collected from various satellites. Hence, the task of target-based data retrieval and acquisition has become exceedingly challenging. Existing satellites essentially scan a vast overlapping region of the Earth using various sensing techniques, like multi-spectral, hyperspectral, Synthetic Aperture Radar (SAR), video, and compressed sensing, to name a few.