This year at CES, Nvidia presented the next generation of its DLSS upscaling technology, which is trained with the help of artificial intelligence, alongside the new GeForce RTX 5090, 5080, and 5070 (Ti) graphics cards. The company touted its major advantages -- and now that RTX 5090 reviews are live, we can confirm that DLSS 4 indeed feels like black magic, supercharging frame rates and making games feel just as snappy as the beloved Doom 2016. That's because DLSS 4 now supports Multi Frame Generation (MFG), an AI-based multiple intermediate frame calculation that can artificially generate up to three images and insert them between two "real" frames, thus quadrupling the frame rate. Of course, this feature only works on new Blackwell-based RTX 50-series GPUs. But are the AI frames generated in this way a step forward or is it all hogwash?

Partially spoofed audio detection is a challenging task, lying in the need to accurately locate the authenticity of audio at the frame level. To address this issue, we propose a fine-grained partially spoofed audio detection method, namely Temporal Deepfake Location (TDL), which can effectively capture information of both features and locations. Specifically, our approach involves two novel parts: embedding similarity module and temporal convolution operation. To enhance the identification between the real and fake features, the embedding similarity module is designed to generate an embedding space that can separate the real frames from fake frames. To effectively concentrate on the position information, temporal convolution operation is proposed to calculate the frame-specific similarities among neighboring frames, and dynamically select informative neighbors to convolution. Extensive experiments show that our method outperform baseline models in ASVspoof2019 Partial Spoof dataset and demonstrate superior performance even in the crossdataset scenario.