Face Detection Performance Using CNNs and Bug BonutyProgram (BBP)

Abstract

Bug bounty schemes make use of outside ethical hackers to find and fix a variety of security flaws, guaranteeingquicker and more affordable problem solving. Better confidence in and image of the company in the cybersecurityspace, faster solving issues, and increased community collaboration are some of its results. Computer vision relies onface detection, which has several uses. This article uses convolutional neural networks (CNNs) and an error rewardalgorithm in the facial recognition simulation library to enhance face detection. Trainers trained CNNs to detect facesfrom other visual components and extract human facial traits, making them powerful facial identification tools. Thesenetworks classify and extract face characteristics automatically, obtaining approaching 100% identification rates.CNNs have greater identification rates and easier face-image extraction than earlier methods. Network architecturedetermines its performance, transcending machine learning methodologies. This article suggests a bug reward schemeto discover and resolve bugs in the face recognition library. The program has helped Google find flaws in itsintelligent systems, including model manipulation and adversarial assaults. These activities enhance AI safety andsecurity studies, highlight possible concerns, and promote AI safety. CNN-based facial recognition models enhanceaccuracy and offer advantages over previous approaches. The CNN-based method and Bug Bounty softwareimproved the facial recognition library.