Watermarking for Video Inter-Frame Tamper Detection

Technology advances at the same rate as human beings. Technology comes with both pros and cons. As difficult as it may be to create a video, modifying it is a piece of cake. Today's culture has easy access to video editing tools, making it possible to make changes to video footage. Video fraud cannot be detected by the sagacity of a person. Tampering is the act of making unauthorised changes or additions to video content by tampering with the video's source code or other code. As a result of their frequent use as evidence, digital videos are indispensable in the courtroom. This method uses Particle Imaging Velocimetry (PIV) to locate areas in the video that have been tampered with. Video manipulation may be detected in a variety of ways using this technique.

The Zoom Effect: Exploring the Impact of Video Calling on Appearance Dissatisfaction and Interest in Aesthetic Treatment During the COVID-19 Pandemic

Background The popularity of videoconferencing platforms has skyrocketed during the COVID-19 pandemic, however, there have been concerns regarding the potential for video calls to promote appearance dissatisfaction, as individuals are exposed to their reflection on camera for extended periods. Objectives The current study characterized current video usage behaviors and their relationship with appearance dissatisfaction and interest in aesthetic procedures in the general population. Methods An online survey was completed by 335 adults currently living in Australia. Multiple aspects of video usage were assessed, including engagement in ‘video manipulation’ techniques to enhance appearance and the focus of visual attention while on video calls (ie, on self or others). The Dysmorphic Concern Questionnaire was administered to determine if video use behaviors were associated with greater body image disturbance. Results Over 1/3 of participants had identified new appearance concerns while on video. Dysmorphic concern was associated with self-focused attention, greater engagement in video manipulation behaviors, and increasing appearance concerns due to their time on video calls. Individuals who identified new video-based appearance concerns reported greater interest in obtaining future beauty treatments (eg, waxing) and aesthetic procedures (eg, non-surgical procedures such as anti-wrinkle injections). Conclusions This is one of first empirical studies to report the potential consequences of video call usage for increasing appearance dissatisfaction and dysmorphic concern, and to demonstrate a link between the use of video calls and interest in cosmetic procedures.

Exposing Vulnerabilities of Deepfake Detection Systems with Robust Attacks

Recent advances in video manipulation techniques have made the generation of fake videos more accessible than ever before. Manipulated videos can fuel disinformation and reduce trust in media. Therefore detection of fake videos has garnered immense interest in academia and industry. Recently developed Deepfake detection methods rely on Deep Neural Networks (DNNs) to distinguish AI-generated fake videos from real videos. In this work, we demonstrate that it is possible to bypass such detectors by adversarially modifying fake videos synthesized using existing Deepfake generation methods. We further demonstrate that our adversarial perturbations are robust to image and video compression codecs, making them a real-world threat. We present pipelines in both white-box and black-box attack scenarios that can fool DNN based Deepfake detectors into classifying fake videos as real. Finally, we study the extent to which adversarial perturbations transfer across different Deepfake detectors and create more accessible attacks using universal adversarial perturbations that pose a very feasible attack scenario since they can be easily shared amongst attackers.

The ‘Zoom Effect’: Exploring the impact of video-calling on appearance dissatisfaction and interest in cosmetic treatment during the COVID-19 pandemic

The popularity of videoconferencing platforms has skyrocketed during the COVID-19 pandemic, however, there have been concerns regarding the potential for video-calls to promote appearance dissatisfaction, as individuals are exposed to their reflection on camera for extended periods. The current study characterised current video-usage behaviours and their relationship with appearance dissatisfaction and interest in beauty procedures in the general population. An online survey was completed by 379 adults currently living in Australia. Multiple aspects of video usage were assessed, including engagement in ‘video-manipulation’ techniques to enhance appearance and the focus of visual attention while on video-calls (i.e. on self or others). The Dysmorphic Concern Questionnaire was administered to determine if video-use behaviours were associated with greater body image disturbance. Over one-third of participants had identified new appearance concerns while on video. Dysmorphic concern was associated with self-focused attention, video manipulation behaviours and increasing appearance concerns. Individuals who identified new video-based appearance concerns reported greater interest in obtaining future beauty and cosmetic procedures. This is the first study to report the potential risks of video-call usage on body image and appearance dissatisfaction. Further research is needed to understand how best to mitigate these risks, as COVID-19 accelerates a virtual age of communication.

MENDETEKSI ORISINALITAS FILE VIDEO MENERAPKAN METODE MD5

Video is one of the multimedia content. In the video the video is stored in a file that can be changed intentionally or unintentionally. To ensure video content does not change, a mechanism is needed to detect the integrity of the video content both from the video quality and the size of the video file. In this research, video manipulation with change in duration, addition of frame and change in video file extension aims to compare the original video recording and manipulation video recording. Based on Hasher Pro testing results show that the system is running well, successfully detecting changes that occur in video files scanning video files with a percentage of 100%, all changes can be detected by the Message Digest (MD5) algorithm on the hash value.Keywords: Video, Metode Massage Digest 5 (MD5), Hasher Pro.