Real-time deepfake detection has become a critical cybersecurity challenge, as increasingly sophisticated deepfake technologies undermine trust in digital communication and media. Users currently lack effective tools that are integrated directly into their browsers and are unable to quickly verify the authenticity of images they encounter daily. Existing solutions struggle to detect newer, diffusion-generated deepfakes, such as those produced by DALL-E and Stable Diffusion, due to high computational requirements and limitations in detection accuracy. To address these challenges, we introduce VeriPhoto Version 2, a browser-based Chrome extension that leverages DistilDIRE, a lightweight transformer-based model specifically optimized for real-time deepfake detection. Crucially, VeriPhoto Version 2 integrates Apple’s Metal Performance Shaders (MPS) to harness the GPU capabilities present in Mac M1 and M2 chips, enabling rapid, highly accurate detection directly within the browser. This GPU acceleration significantly reduces latency and computational load, making VeriPhoto practical and effective for everyday users to seamlessly identify and combat deepfake images in real-time. This presentation will discuss the journey of how we designed veriPhoto v2 and the hurdles we overcame during its implementation.