Ephemeral Face: Anonymization as a Service

Inspired by the cyclical doom of 'Nightfall', where darkness reveals hidden horrors, and the dueling illusions of 'The Prestige', this project leverages the ubiquity of 'Urban Traffic Data' (specifically, publicly accessible CCTV feeds) to create a niche service. The core concept is 'Ephemeral Face': a system designed to temporarily anonymize faces captured by public cameras.

The project's story is one of reclaiming privacy in an increasingly surveilled world. Instead of focusing on identification, the system focuses on -avoiding- identification. It detects faces within the video stream from a chosen public camera feed (traffic cams, building security cams streamed online, etc.). Upon detection, the system applies a user-configurable anonymization technique: blurring, pixelation, or even a generative adversarial network (GAN) to replace the face with a generic, non-identifiable face. This anonymized video feed is then either displayed publicly (e.g., re-streamed online) or used for specific internal purposes (e.g., traffic analysis without personally identifiable information).

How it works:
1. Data Acquisition: Select a publicly available camera feed. This could be from open data portals or websites streaming security camera footage.
2. Facial Recognition: Use a pre-trained facial recognition model (e.g., from OpenCV, TensorFlow, PyTorch) to detect faces in each frame of the video stream.
3. Anonymization: Implement chosen anonymization techniques on the detected faces. Start with simple blurring/pixelation. Explore more advanced GAN-based face replacement as a future enhancement.
4. Stream Output: Restream the anonymized video feed. This can be done via a web server or using streaming protocols.
5. Geofencing / Time Fencing: As another layer of privacy, allow the user to only anonymize video footage within certain geographic boundaries within the source feed. For example, only blur faces near a specific residence. Additionally, only anonymize video for certain times of day or specific days of the week. This allows for a niche offering of high-resolution data outside of private geofences or off business hours while ensuring anonymization is always active when required.

Low-Cost & High Earning Potential:
- Low-Cost: Relies on readily available open-source libraries and public data feeds. The initial investment is primarily developer time. The processing can be done on commodity hardware or low-cost cloud instances.
- High Earning Potential: The service can be monetized through various avenues:
- Subscription Model: Offer subscriptions to individuals concerned about privacy who want to watch public streams without the fear of facial recognition.
- B2B Solutions: Offer anonymization as a service to businesses that use public camera feeds for analytics (e.g., traffic pattern analysis, crowd monitoring) and need to comply with privacy regulations (GDPR, CCPA). The geo/time-fencing addition greatly enhances B2B prospects.
- Custom Integrations: Develop custom integrations for specific camera systems or platforms.
- White-label solution: Provide a white-label anonymization platform to surveillance companies that they can offer as a feature to their existing clientele.