TalkingHeadBench: A Multi-Modal Benchmark & Analysis of Talking-Head DeepFake Detection

The rapid advancement of talking-head deepfake generation fueled by advanced generative models has elevated the realism of synthetic videos to a level that poses substantial risks in domains such as media, politics, and finance. However, current benchmarks for deepfake talking-head detection fail to reflect this progress, relying on outdated generators and offering limited insight into model robustness and generalization. We introduce TalkingHeadBench, a comprehensive multi-model multi-generator benchmark and curated dataset designed to evaluate the performance of state-of-the-art detectors on the most advanced generators. Our dataset includes deepfakes synthesized by leading academic and commercial models and features carefully constructed protocols to assess generalization under distribution shifts in identity and generator characteristics. We benchmark a diverse set of existing detection methods, including CNNs, vision transformers, and temporal models, and analyze their robustness and generalization capabilities. In addition, we provide error analysis using Grad-CAM visualizations to expose common failure modes and detector biases. TalkingHeadBench is hosted on https://huggingface.co/datasets/luchaoqi/TalkingHeadBench with open access to all data splits and protocols. Our benchmark aims to accelerate research towards more robust and generalizable detection models in the face of rapidly evolving generative techniques.

Design Of Two Stage CMOS Operational Amplifier in 180nm Technology

In this paper a CMOS two stage operational amplifier has been presented which operates at 1.8 V power supply at 0.18 micron (i.e., 180 nm) technology and whose input is depended on Bias Current. The op-amp provides a gain of 63dB and a bandwidth of 140 kHz for a load of 1 pF. This op-amp has a Common Mode gain of -25 dB, an output slew rate of 32 $V / \mu s$, and a output voltage swing. The power consumption for the op-amp is $300\mu W$.