The field of authentication and deepfake detection is rapidly evolving, with a focus on developing more robust and reliable methods for verifying the authenticity of voices, images, and videos. Recent research has explored the use of inertial sensing, voice morphing, and deep learning techniques to improve the accuracy of authentication systems. One of the key challenges in this area is the ability to detect and prevent deepfakes, which are becoming increasingly sophisticated and realistic. To address this challenge, researchers are developing new methods for detecting deepfakes, including the use of scaling laws, deepfake detection frameworks, and triaging audio forgeries. Notable papers in this area include Beyond the Voice: Inertial Sensing of Mouth Motion for High Security Speech Verification, which presents a novel approach to speech verification using inertial sensing, and Scaling Laws for Deepfake Detection, which analyzes the performance of deepfake detection models and proposes a data-centric approach to improving their accuracy. Additionally, Fit for Purpose? Deepfake Detection in the Real World highlights the limitations of existing deepfake detection models and the need for more realistic and effective evaluation protocols.