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

. Traditional approaches are imprecise and unreliable,

The Contribution: This study introduces CNN-based face identification and evaluates Bug Bounty, a novel facial recognition library vulnerability discovery and patching method.The combination of bug bounty with CNN technology makes this effort more engaging and comprehensive in enhancing computer vision security and performance.
Many applications require good computer vision face identification, yet traditional approaches may be incorrect.This paper solves these vulnerabilities utilizing a bug reward program and CNN-based approach to secure the face recognition library.
The Outline of the Paper: Section Two: Literature review.Section Three: Methodology.Section Four: The proposed methodology.Section Five: Conclusion and recommendations for future work.

RELATED WORKS
The following are some related works: This article [7] presents A bug bounty case study showing how they may find software security issues.Bug bounty research and corporate security advantages will be assessed.Successful bug bounty schemes that found important vulnerabilities are evaluated qualitatively.Bug bounty hunting appears to find security flaws in gadgets, mobile apps, network protocols, and Internet apps.Skilled security researchers are valued .
In [8], digital platforms implement Bug Bounty Programs (BBPs) to improve software reliability following a rise in security breaches of third-party applications.BBPs help platforms and sellers but may increase prices and impact adoption incentives for suppliers, according to the report.A methodology is presented for evaluating the strategic decisions of platforms and external suppliers during BBP launch and engagement.Security breach loss and vendor investment efficiency are key variables in these choices.The article found that the use of BBP is only balanced when the potential loss is high and the investment efficiency is low.In some settings, BBPs may reduce software stability, platform reliability, and end-user experience, making them socially disadvantageous .
In [9], user confidence in the security of online data is essential to the smooth operation of digital markets and societies, where security is fundamental.Given the complexity and cost of cybersecurity threats, more companies and governments are using bug bounty programs (BBPs) to improve cybersecurity.Hackers are paid by BBP to reveal system vulnerabilities.
In [10], this study evaluates whether a bug bounty program reduces data breaches and how a company's appetite for risk affects this communication.Study reveals that bug bounty schemes encourage data breaches.A bug bounty program reduces data breaches for risk-averse companies, although this benefit is reduced by risk aversion.The study adds to the knowledge on crowdsourcing and cybersecurity and provides practitioners with useful tips.
In [11], the paper covers bug bounty services like Hacker One that outsource vulnerability disclosure to hackers.The paper outlines the costs and benefits for companies and hackers.Running a bug bounty program for a year costs less than hiring two software developers, highlighting its cost-efficiency in resolving vulnerabilities.

PROPOSED SYSTEM
The general steps of the proposed methodology are:

Data Preparation:
• Gather a dataset of approved facial images for training.
• Data set preprocessing, including resizing, normalization and data enhancement.

CNN Model Structure:
• Determine the structure of a convolutional neural network (CNN) suitable for face detection.
• Create layers for convolution, pooling, and fully connected layers.
• Add activation functions, such as ReLU.
• Compile the model using appropriate loss functions and optimizers.

Model Training:
• Split the data set into training and validation sets.
• Train the CNN model on the training data.
• Monitor training progress and adjust hyper parameters as needed.

Evaluation:
• Evaluates the trained model on a separate test dataset.
• Calculate precision, precision, recall, and F1 score for face detection.Rephrase it linguistically and replace it with synonyms The steps as shown in Figure 1.

Implementing a Bug Bounty Program in a Face Detection Library Preparation and Implementation
Steps:

Setting up the Emulation Library:
• Create a Python library that simulates the face detection function.
• Include known vulnerabilities or issues in the library code.

Error Reporting Mechanism:
• Developed a reporting system that allows users to submit bug reports.
• Include a form for users to explain issues and upload code or sample data.

Bug Tracking:
• Create a bug-tracking system to record and categorize incoming bug reports.
• Assign severity levels to each reported issue.

Bug Fix:
• Develop a process to address reported errors.
• Prioritize errors and fix them based on their severity.
• Update library code with bug fixes.

Bug Bounties:
• Set rewards or incentives for users who report valid vulnerabilities.
• Implement a mechanism to distribute rewards to successful informants.

Documentation:
• Provide clear documentation for the library, including how to report bugs and participate in the Bug Bounty program.
The use case of the proposed method is shown in Figure 2 Data Preparation

Evaluating the effectiveness of a bug bounty program in detecting defects in facial recognition algorithms :
This approach replicates bug bounty via false facial detection.This study tests a bug bounty program's facial recognition algorithm defect detection.The system contains face detection, quality evaluation metrics, bug bounty simulation, vulnerability assessment, and results presentation simulation libraries.

3.2.1
Face detection simulation library: This custom library simulates facial detection.To simulate real-world facial recognition systems, this library has intended shortcomings.The library correctly and incorrectly recognizes facial positions.

3.2.2
Quality measurement standards: The quality metrics system assesses bug bounty performance.It assesses detection accuracy and completeness using precision and recall.Accuracy is the percentage of faces properly identified, whereas recall is the percentage of faces discovered.

3.2.3
Simulation Bug Bounty Technology: This method employs a face detection library and real faces with known locations.The error reward simulation uses the simulation library to test face detection.Comparing recognized faces to actual faces yields accuracy and recall measurements.

3.2.4
Vulnerability assessment: After detection, specific criteria are used to evaluate the vulnerability.Vulnerabilities are identified if the number of recognized faces exceeds a specified minimum, indicating flaws in the detection algorithm.

3.2.5
Results: The results of the bug bounty simulation are presented in the form of text and images.The bug bounty program aims to report precision, recall, and vulnerability.The performance of the algorithm is demonstrated by displaying detection images that show squares around faces.

3.2.6
Efforts to reward repetitive errors: Ongoing efforts are being made to develop a bug bounty program to test the library's performance.Face detection simulation, quality metrics, vulnerability assessment, and results display are included in each iteration.Figure 3 shows the class diagram of the proposed software.

Conclusion and future work
The suggested face detection library simulation method evaluates error reward programs with high accuracy.The efficiency of bug bounty schemes may be assessed using simulation technologies and real-world occurrences.Recall and accuracy assess detection quality and efficiency.Accuracy is defined as the ratio of positive detections (either true or false) relative to all detected vulnerabilities.The recall metric reflects the percentage of true vulnerabilities, whether detected or not, that lead to true positive outcomes out of all good scenarios.This evaluation adds complexity to measuring the success of a bug bounty program.Regarding the flexibility of the face detection algorithm, a vulnerability assessment is included to indicate technical issues that improve the security of the system.A bug bounty effort is to frequent evaluation, which makes it easier to measure the success of the program and stabilize the library.Presenting the results of the bug bounty program through graphs and text reports helps understand how it detects flaws in the facial recognition algorithm.
Face detection systems are in demand in social media and security systems, hence it is suggested to enhance them: 1. Improving the Simulation Environment: Complex factors like illumination and viewing angles improve simulation accuracy.

Improving Assessment Criteria:
Adding new criteria to better assess bug bounty program performance.

Expanding Security Vulnerabilities:
Analyze and evaluate more security vulnerabilities to improve face detection system security.

Using Machine Learning:
Using machine learning to increase bug reward and detection accuracy.

FIGURE 2 .
FIGURE 2. Use Case Diagram for the Proposed Method

FIGURE 3
FIGURE 3. Class Diagram for the Proposed Program

Table 2 :
-Illustrates the combined results of face detection and bug bounty simulation

Table 1 .
Simulated Face Detection Library Results