PH Deck

Users struggle to identify and locate specific individuals on the internet using images, which can be crucial for purposes like security, networking, or personal curiosity.

Users are unaware of where their facial images appear online, such as on random blogs, fake accounts, or obscure corners of the internet. They struggle to track unauthorized use of their face, risking privacy breaches and identity misuse.

Users struggle to create unique and personalized human faces for various purposes, such as art, design, and digital content creation

Users currently rely on manual methods or basic software for face swapping in videos, photos, and GIFs. The drawbacks of these methods are that they are often time-consuming, require technical skills, and frequently result in unprofessional and poor-quality outputs.

Users need to manually blur faces in photos using image editing software or basic tools, requiring time-consuming manual editing and technical expertise. This process becomes inefficient when handling multiple faces.

Users previously relied on manual video editing tools requiring technical skills and time-consuming manual editing, or faced face swap applications with usage limits or mandatory sign-ups

Users previously relied on manual photo/video editing tools (e.g., Photoshop) or basic face-swapping apps for creative content. Requires technical skills, time-consuming, and often produces unrealistic results

The current situation for users is the need to track where their photos appear online. This typically involves manually searching the internet, which can be time-consuming and inefficient.

Users currently have to use downloadable software or apps to perform face swaps, which often require technical skills to operate and may not support multiple media formats. These solutions are often limiting, forcing users to deal with issues such as complicated interfaces and lengthy processing times. The drawback is the need to download software, risk of watermarks, and format limitations.

Users need to manually or with outdated APIs manage face recognition tasks, resulting in slow processing, high costs, and inaccuracies in real-time identification.