Keyframe insertion : enabling low-latency random access and packet loss repair

From a video coding perspective, there are two challenges when performing live video distribution over error-prone networks, such as wireless networks: random access and packet loss repair. There is a scarceness of solutions that do not impact steady-state usage and users with reliable connections. The proposed solution minimizes this impact by complementing a compression-efficient video stream with a companion stream solely consisting of keyframes. Although the core idea is not new, this paper is the first work to provide restrictions and modifications necessary to make this idea work using the High-Efficiency Video Coding (H.265/HEVC) compression standard. Additionally, through thorough quantification, insight is provided on how to provide low-latency fast channel switching capabilities and error recovery at low quality impact, i.e., less than 0.94 average Video Multimethod Assessment Fusion (VMAF) score decrease. Finally, worst-case drift artifacts are described and visualized such that the reader gets an overall picture of using the keyframe insertion technique

Fast fallback watermark detection using perceptual hashes

Forensic watermarking is often used to enable the tracing of digital pirates that leak copyright-protected videos. However, existing watermarking methods have a limited robustness and may be vulnerable to targeted attacks. Our previous work proposed a fallback detection method that uses secondary watermarks rather than the primary watermarks embedded by existing methods. However, the previously proposed fallback method is slow and requires access to all watermarked videos. This paper proposes to make the fallback watermark detection method faster using perceptual hashes instead of uncompressed secondary watermark signals. These perceptual hashes can be calculated prior to detection, such that the actual detection process is sped up with a factor of approximately 26,000 to 92,000. In this way, the proposed method tackles the main criticism about practical usability of the slow fallback method. The fast detection comes at the cost of a modest decrease in robustness, although the fast fallback detection method can still outperform the existing primary watermark method. In conclusion, the proposed method enables fast and more robust detection of watermarks that were embedded by existing watermarking methods

A novel video watermarking approach based on implicit distortions

Copyright-sensitive videos are commonly leaked or illegally distributed by so-called digital pirates. Video owners aim to prevent this by hiding a unique watermark in every video that contains information about the receiver. If the video is then illegally distributed, the copyright owner can extract the watermark and identify the malicious consumer. However, pirates may manipulate the video in the hope of destroying the embedded watermark. Although a variety of imperceptible and robust solutions exist, these introduce many artificial distortions to the video. Therefore, this paper proposes a novel video watermarking approach in which only a single encoder decision is explicitly changed. Then, the explicit change automatically propagates into a large collection of implicit distortions that represents the watermark. The implicit distortions resemble ordinary, encoder-created compression artifacts and hence are imperceptible. Additionally, they prove to be robust against video manipulations. Furthermore, the proposed scheme requires no modification of existing consumer electronic devices. Consequently, the proposed watermarking approach can be applied to help combat piracy without bothering innocent users with unnatural distortions

Training Data Improvement for Image Forgery Detection using Comprint

Manipulated images are a threat to consumers worldwide, when they are used to spread disinformation. Therefore, Comprint enables forgery detection by utilizing JPEG-compression fingerprints. This paper evaluates the impact of the training set on Comprint's performance. Most interestingly, we found that including images compressed with low quality factors during training does not have a significant effect on the accuracy, whereas incorporating recompression boosts the robustness. As such, consumers can use Comprint on their smartphones to verify the authenticity of images.Comment: Will be presented at the International Conference on Consumer Electronics (ICCE) 2023 in Las Vegas, NV, US

Fast and Blind Detection of Rate-Distortion-Preserving Video Watermarks

Forensic watermarking enables the tracing of digital pirates that leak copyright-protected multimedia. To prevent a negative impact on the video quality or bit rate, rate-distortion-preserving watermarking exists, which represents a watermark as compression artifacts. However, this method has two main disadvantages; the detection has a high complexity and it is non-blind. Although a method based on perceptual hashing exists that speeds up the detection of a fallback watermarking system, it decreases its robustness. Therefore, this paper proposes a novel fast detection method that has less impact on the robustness than related work. Our method optimized NS-DCT-DST hashes for rate-distortion-preserving watermarking, which are more robust to content-preserving attacks. Moreover, a blind version is proposed which does not require the original video for hash extraction. As such, the detection is up to 5700 times faster, at the cost of a modest decrease in robustness. In fact, the proposed method shows good robustness to content preserving recompression attacks when using hashes that are as small as 432 bytes. This is much smaller than comparable performance of related work. In conclusion, this paper enables fast adversary tracing using watermarks that do not impact the video’s compression efficiency

Shedding light into the need of knowledge sharing in H2020 thematic networks for the agriculture and forestry innovation

Providing ready-for-practice materials in an easily accessible and user-friendly way is one of the challenges of Horizon 2020 Thematic Networks (TNs) to promote agriculture and forestry innovation. Those materials are the result of the co-creation and knowledge exchange among TN actors. Using a survey, we investigated the most efficient communication and dissemination (C&D) channels, types and formats of TN-produced data, as well as the involvement of TN actors. Additionally, we propose to collect TN outputs in a single digital platform, such that they are more Findable, Accessible, Interoperable, and Reusable (FAIR), ensuring the TN's impact. An open and interactive platform may allow us to overcome challenging issues such as language barriers, limited Internet access and differing cultural backgrounds. The results obtained from the survey suggest which content should be stored in such a digital knowledge reservoir for agriculture and forestry. For example, newsletters and visual materials are often preferred by farmers and foresters. Additionally, face-to-face interaction is confirmed to be the most preferred way of retrieving information, especially by researchers and farmers/foresters. Future work will further investigate the variations in needs of different user profiles. As such, the proposed platform can stimulate knowledge exchange among all TN actors