Streamed Video Reconstruction for Firefox Browser Forensics

In criminal investigations, the digital evidence extracted from social media may provide exceptional support. Reviewing the history or cache of the web browser may provide a valuable insight into the activity of the suspect. The growing popularity of Internet video streaming creates a risk of this technology misuse. There are a few published research on video reconstruction forensics on the Chrome browser. There is a difference in the methods applied to reconstruct cached video on Chrome from the methods applied to Firefox or any browser. Our primary focus in this research is to examine the forensic procedures required to reconstruct cached video stream data using Twitter and YouTube on the Firefox browser. Some work has been done to reconstruct a cached video on the Chrome browser, but we need more work on the rest of the browsers, most notably the Firefox browser used in this research. Both examination strategies and contemplations displayed are approved and suitable for the forensic study of various streaming platforms as well as the web browser caches.

A Multi-instance Multi-label Dual Learning Approach for Video Captioning

Video captioning is a challenging task in the field of multimedia processing, which aims to generate informative natural language descriptions/captions to describe video contents. Previous video captioning approaches mainly focused on capturing visual information in videos using an encoder-decoder structure to generate video captions. Recently, a new encoder-decoder-reconstructor structure was proposed for video captioning, which captured the information in both videos and captions. Based on this, this article proposes a novel multi-instance multi-label dual learning approach (MIMLDL) to generate video captions based on the encoder-decoder-reconstructor structure. Specifically, MIMLDL contains two modules: caption generation and video reconstruction modules. The caption generation module utilizes a lexical fully convolutional neural network (Lexical FCN) with a weakly supervised multi-instance multi-label learning mechanism to learn a translatable mapping between video regions and lexical labels to generate video captions. Then the video reconstruction module synthesizes visual sequences to reproduce raw videos using the outputs of the caption generation module. A dual learning mechanism fine-tunes the two modules according to the gap between the raw and the reproduced videos. Thus, our approach can minimize the semantic gap between raw videos and the generated captions by minimizing the differences between the reproduced and the raw visual sequences. Experimental results on a benchmark dataset demonstrate that MIMLDL can improve the accuracy of video captioning.

Joint Subtitle Extraction and Frame Inpainting for Videos with Burned-In Subtitles

Subtitles are crucial for video content understanding. However, a large amount of videos have only burned-in, hardcoded subtitles that prevent video re-editing, translation, etc. In this paper, we construct a deep-learning-based system for the inverse conversion of a burned-in subtitle video to a subtitle file and an inpainted video, by coupling three deep neural networks (CTPN, CRNN, and EdgeConnect). We evaluated the performance of the proposed method and found that the deep learning method achieved high-precision separation of the subtitles and video frames and significantly improved the video inpainting results compared to the existing methods. This research fills a gap in the application of deep learning to burned-in subtitle video reconstruction and is expected to be widely applied in the reconstruction and re-editing of videos with subtitles, advertisements, logos, and other occlusions.

Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity

Reconstructing complex and dynamic visual perception from brain activity remains a major challenge in machine learning applications to neuroscience. Here we present a new method for reconstructing naturalistic images and videos from very large single-participant functional magnetic resonance data that leverages the recent success of image-to-image transformation networks. This is achieved by exploiting spatial information obtained from retinotopic mappings across the visual system. More specifically, we first determine what position each voxel in a particular region of interest would represent in the visual field based on its corresponding receptive field location. Then, the 2D image representation of the brain activity on the visual field is passed to a fully convolutional image-to-image network trained to recover the original stimuli using VGG feature loss with an adversarial regularizer. In our experiments, we show that our method offers a significant improvement over existing video reconstruction techniques.