Welcome to Cavernous Hoax Scanner, a cutting-edge tool designed to detect and analyze deepfake images. Deepfakes are synthetic media where a person in an existing image or video is replaced with someone else's likeness. With the increasing sophistication of deepfake technology, it's crucial to have reliable methods to identify and mitigate potential misuse.
Our aim is to provide a robust system that:
- Detects whether an image is a deepfake.
- Classifies the level of harm or vulnerability associated with the detected deepfake.
- Provides explanations for why a deepfake is deemed harmful or vulnerable.
- Image Upload: Users can upload an image through our intuitive React-based interface.
- Processing: The image is sent to our Node.js backend, where it is then processed using advanced Python-based machine learning models.
- Detection: The system analyzes the image to determine if it's a deepfake.
- Classification: If a deepfake is detected, the system further classifies its potential harm and provides detailed reasons for its assessment.
- Output: Results are displayed back on the user interface, along with any pertinent information about the deepfake's vulnerability.
- Frontend: Embadded.js for a modern, responsive, and user-friendly interface.
- Backend: Node.js for efficient handling of API requests and server-side logic.
- Machine Learning: Python for deep learning models that detect and classify deepfakes.
- Database: JsonDB for storing user data and analysis results.
- APIs: Our system includes APIs (CHSAPI) for integration with other systems and platforms.
- JavaScript (Embadded, Node.js)
- TypeScript (Helping hand of backend, Secuity counters)
- Python (Machine Learning Models)
- Json (Level Data Manipulation)
- HTML/CSS (UI/UX Design)
- Frontend Developer: Souvik Kar
- UI/UX Developer: Rajanya Chakraborty
- Backend Developer/Lead Developer: Krishnendu Mitra
- Machine Learning Specialist: Saptarshi Pramanik
In an era where digital media is omnipresent, the misuse of deepfake technology poses significant threats to privacy, security, and trust. Our project aims to safeguard individuals and organizations by providing a reliable tool to detect and analyze deepfakes, ensuring that any malicious content can be identified and addressed promptly.
Cavernous Hoax Scanner's perfromance and secuirty feature is so-high thats why facebook use CHSAPI on there platform to remove deepfake media and transfer learning the knowlage to there models for future use. Not only facebook but also so many other brands like github, mockmantra, godaddy are also use Cavernous Hoax Scanner's API (CHSAPI) in there system.
CHS use so many security check points on media uploading, transfer, authentication, and etc.. task. Basically CHS convert a uploaded media into base64 string and then perform subsitution chiper on that string and send it to the internet with hash function value and server recive it and calculate this hash function and decode the base64 for task performing. And it use secure channels to send data with SHA-256 algorithm. This approch defened CHS from various attacks like - DDOS, Malware, Man-in-the-Middle, Brute-Force, SQL-Injection, Traffic, Masquerade, etc..
For inquiries, collaborations, or feedback, please reach out to us at:
- Email: info.whitelotus24@gmail.com
- LinkedIn: WHITE LOTUS
- Twitter: @whitelotus
Feel free to contact us