Face Detection Performance Using CNNs and Bug Bonuty Program (BBP)

Abstract

Bug bounty schemes make use of outside ethical hackers to find and fix a variety of security flaws, guaranteeing quicker and more affordable problem solving. Better confidence in and image of the company in the cybersecurity space, faster solving issues, and increased community collaboration are some of its results. Computer vision relies on face detection, which has several uses. This article uses convolutional neural networks (CNNs) and an error reward algorithm in the facial recognition simulation library to enhance face detection. Trainers trained CNNs to detect faces from other visual components and extract human facial traits, making them powerful facial identification tools. These networks 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 architecture determines its performance, transcending machine learning methodologies. This article suggests a bug reward scheme to discover and resolve bugs in the face recognition library. The program has helped Google find flaws in its intelligent systems, including model manipulation and adversarial assaults. These activities enhance AI safety and security studies, highlight possible concerns, and promote AI safety. CNN-based facial recognition models enhance accuracy and offer advantages over previous approaches. The CNN-based method and Bug Bounty software improved the facial recognition library.

Keywords

• Bug Bounty program
• vulnerabilities
• robustness
• convolutional neural networks (CNNs)
• face detection