Deceptive Identity: AI-Powered Facial Disguise Detector

Inspired by the 'I, Robot' focus on nuanced robotic perception and the 'Tenet' concept of inverted time, 'Deceptive Identity' aims to identify attempts to manipulate facial recognition systems. The project starts with scraping fashion catalogs (similar to the initial 'Fashion Catalogs' scraper project inspiration) to gather a large dataset of faces with various makeup styles and accessories. This dataset is then augmented with synthetically generated images featuring digitally altered faces and realistic prosthetics. The core of the system is a two-pronged approach. First, a standard facial recognition model is trained and fine-tuned. Second, an adversarial network is trained to identify manipulated images intended to fool the first model. This adversarial network is trained on the augmented dataset, learning to spot inconsistencies and subtle 'digital artifacts' often missed by humans. The 'Tenet' influence comes into play through a time-series analysis component. This component analyzes video feeds, looking for temporal inconsistencies in facial features. For example, sudden changes in perceived age, subtle morphing of features frame-by-frame, or discrepancies in lighting and shadows that might indicate digital manipulation. This is crucial for detecting 'living' deepfakes or real-time facial disguises. The project can be implemented with readily available libraries like OpenCV, TensorFlow, and PyTorch. The niche lies in its focus on detecting -attempts- to deceive, rather than simply identifying faces. The low-cost aspect comes from using open-source tools and publicly available datasets (augmented with scraped fashion catalog data and synthetic data). The earning potential is high because it caters to the growing demand for security solutions capable of detecting sophisticated forms of identity fraud and disinformation. Potential applications include enhanced security for financial institutions, anti-fraud measures for online platforms, and law enforcement applications for identifying individuals using disguises.