Exfiltrating data from an air-gapped system through a screen-camera covert channel

In recent years, many methods of exfiltrating information from air-gapped systems, including electromagnetic, thermal, acoustic and optical covert channels, have been proposed. However, as a typical optical channel, the screen-camera method has rarely been considered; it is less covert because it is visible to humans. In this paper, inspired by the rapid upgrades of cameras and monitors, we propose an air-gapped screen-camera covert channel with decreased perceptibility that is suitable for complex content. Our method exploits the characteristics of the human vision system (HVS) and embeds quick response (QR) codes containing sensitive data in the displayed frames. This slight modification of the frames cannot be sensed by the HVS but can be recorded by the cameras. Then, using certain image processing techniques, we reconstruct the QR codes to some degree and extract the secret data with a certain level of robustness due to the error correction capacity of QR codes. In the scenario to which our method applies, we assume that a program has been installed in the target system and has the authority to modify the frames without affecting the normal operations of valid users. Cameras, such as web cameras, surveillance cameras and smartphone cameras, can be receivers in our method. We illustrate the applicability of our method to frames with complex content using several different cover images. Experiments involving different angles between the screen and the camera were conducted to highlight the feasibility of our method with angles of 0°,15° and 30°

An Audio Steganography Based on Run Length Encoding and Integer Wavelet Transform

This paper proposes an audio steganography method based on run length encoding and integer wavelet transform which can be used to hide secret message in digital audio. The major contribution of the proposed scheme is to propose an audio steganography with high capacity, where the secret information is compressed by run length encoding. In the applicable scenario, the main purpose is to hide as more information as possible in the cover audio files. First, the secret information is chaotic scrambling, then the result of scrambling is run length encoded, and finally, the secret information is embedded into integer wavelet coefficients. The experimental results and comparison with existing technique show that by utilizing the lossless compression of run length encoding and anti-attack of wavelet domain, the proposed method has improved the capacity, good audio quality, and can achieve blind extraction while maintaining imperceptibility and strong robustness.</jats:p

DreamFactory : Pioneering Multi-Scene Long Video Generation with a Multi-Agent Framework

Current video generation models excel at creating short, realistic clips, but struggle with longer, multi-scene videos. We introduce \texttt{DreamFactory}, an LLM-based framework that tackles this challenge. \texttt{DreamFactory} leverages multi-agent collaboration principles and a Key Frames Iteration Design Method to ensure consistency and style across long videos. It utilizes Chain of Thought (COT) to address uncertainties inherent in large language models. \texttt{DreamFactory} generates long, stylistically coherent, and complex videos. Evaluating these long-form videos presents a challenge. We propose novel metrics such as Cross-Scene Face Distance Score and Cross-Scene Style Consistency Score. To further research in this area, we contribute the Multi-Scene Videos Dataset containing over 150 human-rated videos