Generative AI has made text-guided inpainting a powerful image editing tool, but at the same time a growing challenge for media forensics. Existing benchmarks, including our text-guided inpainting forgery (TGIF) dataset, show that image forgery localization (IFL) methods can localize manipulations in spliced images but struggle in fully regenerated (FR) images, while synthetic image detection (SID) methods can detect fully regenerated images but cannot perform localization. With new generative inpainting models emerging and the open problem of localization in FR images remaining, updated datasets and benchmarks are needed. We introduce TGIF2, an extended version of TGIF, that captures recent advances in text-guided inpainting and enables a deeper analysis of forensic robustness. TGIF2 augments the original dataset with edits generated by FLUX.1 models, as well as with random non-semantic masks. Using the TGIF2 dataset, we conduct a forensic evaluation spanning IFL and SID, including fine-tuning IFL methods on FR images and generative super-resolution attacks. Our experiments show that both IFL and SID methods degrade on FLUX.1 manipulations, highlighting limited generalization. Additionally, while fine-tuning improves localization on FR images, evaluation with random non-semantic masks reveals object bias. Furthermore, generative super-resolution significantly weakens forensic traces, demonstrating that common image enhancement operations can undermine current forensic pipelines. In summary, TGIF2 provides an updated dataset and benchmark, which enables new insights into the challenges posed by modern inpainting and AI-based image enhancements. TGIF2 is available at https://github.com/IDLabMedia/tgif-dataset .

The demand for high-quality, low-latency video streaming is placing strain on conventional internet infrastructures. This article proposes a hybrid unicast–broadcast video delivery framework designed to address this challenge by integrating advanced 5G broadcast technologies with traditional unicast methods. By offloading popular content to a broadcast network, the approach aims to alleviate congestion and enhance overall streaming efficiency. To ensure reliable video segment delivery over the broadcast network, regardless of the physical layer, we incorporate Packet Recovery (PR) and Forward Error Correction (FEC) mechanisms. Additionally, Temporal Layer Injection (TLI) is employed to further improve video quality while maintaining reduced bandwidth requirements compared to traditional unicast-only approaches. This innovative framework leverages 5G terrestrial broadcasting within Over-the-Top (OTT) streaming environments, enabling seamless delivery of adaptive video content with sub-1-second live latency. Comprehensive experimentation and evaluation through large-scale emulation demonstrate the efficacy of this hybrid approach in meeting the evolving demands of modern multimedia delivery systems. Notably, when broadcasting the top three most commonly watched video streams, 63% of viewers no longer need to request video segments via unicast, as they are efficiently delivered over broadcast channels. This hybrid model offers significant scalability, cost reduction for an ISP, and efficiently delivers content directly to user devices without additional intermediaries, improving viewer experience through low-latency, high-quality streaming.

Real-time video encoding requires efficient and accurate quality metrics to optimize performance under strict computational and latency constraints. Traditional low-complexity metrics such as PSNR and SSIM often fall short in perceptual alignment, while accurate metrics such as VMAF are too computationally intensive for real-time deployment.We present x265-pVMAF, a low-complexity perceptual quality metric integrated into the x265 encoding loop. By leveraging machine learning and efficiently extracted encoder features, x265-pVMAF bridges the gap between computational efficiency and perceptual accuracy. It replicates VMAF predictions with a correlation of 0.99, while delivering a 37× speed-up. These results establish x265-pVMAF as a practical solution for real-time video quality assessment in next-generation encoding workflows.

In response to the growing demand for low-latency video streaming, this paper presents a demonstration of a hybrid unicast-broadcast video delivery system that combines 5G terrestrial broadcasting with over-the-top (OTT) streaming methods. The demonstration features a scalable setup with an interactive dashboard, allowing users to experiment with various configurations and observe key metrics such as bandwidth usage, packet loss, buffer size, and live latency in real-time. Key techniques include Low-Latency DASH (LL-DASH) for HTTP Adaptive Streaming (HAS), packet recovery (PR) and Forward Error Correction (FEC) for reliability, Temporal Layer Injection (TLI) for enhanced quality, and Common Media Application Format (CMAF) with Chunked Transfer Encoding (CTE) for reduced latency. The demonstration shows that this scalable hybrid approach can effectively reduce unicast bandwidth to nearly 0 Mb/s in scenarios without packet loss on the broadcast network, and achieve similar bandwidth reductions in lossy broadcast networks with appropriate Forward Error Correction (FEC) settings, while maintaining a live latency lower than 1 second. These results demonstrate the system's potential for optimizing multimedia delivery, significantly reducing unicast bandwidth while maintaining low-latency streaming.

The increasing demand for high-quality video streaming, coupled with the necessity for low-latency delivery, presents significant challenges in today's multimedia landscape. In response to these challenges, this research explores the optimization of adaptive video streaming by integrating 5G terrestrial broadcasting with over-the-top (OTT) streaming methods. A comprehensive integration of forward error correction (FEC), temporal layer injection (TLI), and broadcast techniques enhance the robustness and efficiency of content delivery over broadcast networks and reduce unicast bandwidth to zero in low loss environments. Multiple strategies are compared through an extensive emulation setup for reducing latency in the end-to-end video delivery chain to sub 3-second live latency, demonstrating the effectiveness of a hybrid unicast-broadcast approach in achieving low-latency while maintaining high-quality video streaming performance with significantly reduced bandwidth. For 62.99% of viewers, unicast bandwidth can be reduced to as low as zero when broadcasting the top 3 TV channels.

Video streaming systems aim to provide high-quality video adapted to clients’ device and network conditions. For this purpose, adaptive streaming architectures encode video content at a variety of quality levels, organized in a bitrate ladder. However, compressing a video into multiple streams is resource-intensive, which may become especially problematic in live streaming applications with real-time demands. Therefore, this paper proposes a novel solution for fast bitrate ladder creation, and provides the requirements for implementation in the H.266/VVC standard. More specifically, the proposed method creates new intermediate Combined Streams by injecting the lowest temporal layers of a higher-quality Augmentation Stream in a lower-quality Base Stream. Since the lowest layers are used as reference by the remaining layers, this procedure indirectly increases the quality of the frames in those untouched remaining layers as well. We demonstrate that injecting more layers brings both the quality and bitrate closer to that of the Augmentation Stream. The disadvantage of the Combined Streams is that their quality fluctuates more than the quality of the source streams, and that they are compressed less efficiently, comparable to going from a slower to fast or faster preset in the VVenC encoder. Most importantly, their main advantage is that they were generated at no significant additional computational complexity. In this way, the proposed method is of great benefit when generating a bitrate ladder of video streams under constrained computational resources.

Applications providing ultra-low-latency video streaming to large audiences require fast and efficient packet-loss repair. Previous methods utilizing keyframe injection (such as the High Efficiency Streaming Protocol) have a low impact on the repaired stream quality, but at a cost of a significant bitrate spike during repair. In this paper, we propose injecting P-frames for packet-loss repair of ultra-low-latency streaming. We implemented (open-source) and evaluated our approach in both H.265/HEVC and H.266/VVC standards. Through extensive evaluations, we demonstrate that the proposed solution significantly reduces bitrate overhead while maintaining a similar or lower decrease in quality compared to existing packet-loss-repair techniques. Overall, the proposed approach offers a promising solution to ensure reliable packet-loss recovery, efficient resource utilization, and a high-quality streaming experience.

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 experimentally measured to be 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 related work at comparable performance. In conclusion, this paper enables fast adversary tracing using watermarks that do not impact the video’s compression efficiency.

Sending low-delay live video over error-prone channels comes with packet-loss-repair and random-access challenges. Existing solutions have a negative impact on end-users with reliable connections or users that do not switch channels. To minimize this impact, the keyframe-insertion technique extends a compression-efficient normal stream (NS) with a companion stream (CS) solely consisting of keyframes [1].

Low-delay live streaming applications desire fast channel switching and packet-loss repair capabilities. However, existing methods that provide these capabilities have a negative impact on the stream of steady-state users. To minimize this impact, techniques such as the High Efficiency Streaming Protocol (HESP) utilize keyframe injection. Such techniques combine compression-efficient normal streams with corresponding companion streams that are used in case of random access or packet loss. Unfortunately, because a companion stream is needed for every normal stream, the distribution cost and encoding complexity are considerable costs. Additionally, injecting a companion keyframe into a normal stream causes a bitrate spike. Therefore, this paper evaluates the impact of utilizing mixed-resolution keyframe injection in the H.266/VVC standard. By providing a single companion stream for all normal streams of a bitrate ladder, the three mentioned downsides can be mitigated. We found that injecting a lower-resolution keyframe effectively reduces the bitrate spike, at the cost of only a modest quality loss. For dynamic video content, the quality impact reduces over time, and is less perceptible than traditional packet-loss repair using frame copy. In conclusion, mixed-resolution HESP can reduce the bitrate spike and computational/distribution overhead of HESP when enabling fast channel switching or packet-loss repair.

Forensic watermarking is used to track down digital pirates after they illegally redistribute video content. Although existing algorithms often resist common signal processing attacks, they are not always robust against camcording attacks. As a solution in the state of the art, registration methods are used to align the attacked video to the original one. However, watermark detection still fails when the quality is sufficiently decreased or when exposed to targeted attacks. Therefore, this paper proposes a novel fallback system that aims to detect the watermark when traditional methods fail. More concretely, we demonstrate that a primary watermark embedded by a traditional scheme indirectly creates a secondary watermark signal during video encoding. This secondary watermark consists of compression artifacts and is detected by the fallback system. Additionally, the proposed system incorporates video registration to cope with camcording attacks. The experimental results indicate that the fallback system has a striking increase in robustness compared to the existing methods. For example, the observed false-negative rate for targeted attacks improves from 100% to 0%. Moreover, the fallback is camcording resistant even when the traditional method combined with registration is not. In conclusion, the proposed system can be used as a fallback when traditional detection fails.

Video watermarking is a well-established technology to help identify digital pirates when they illegally re-distribute multimedia content. In order to provide every client with a unique, watermarked video, the traditional distribution architectures separately encode each watermarked video. However, since these encodings require a high amount of computational resources, such architectures do not scale well to a large number of users. Therefore, this paper proposes a novel architecture that uses fast encoders instead of traditional, full encoders. The fast encoders re-use the coding information from a single, previously-encoded, unwatermarked video in order to speed up the encodings of the watermarked videos. As a result, the complexity of a fast encoder is only a fraction of the complexity of a full encoder. Due to a high correlation of the re-used coding information with the optimal coding information, the compression efficiency and watermark robustness decrease only slightly. Most importantly, the proposed fast encoder speeds up the compression process with a factor of 115, resulting in a low complexity similar to that of a video decoder. Consequently, video distributors can use the proposed architecture to deliver high-quality watermarked videos on a large-scale without requiring an excessive amount of computational resources.