About Our Team
We are a dedicated team of researchers, engineers, and designers committed to combating deepfake audio fraud through advanced AI technology.
Dr. Sonali Mathur
Supervised the team throughout the development of EchoShield, providing guidance on AI ethics, model architecture, and research methodology
Atharv Singh
Trained the BiLSTM model, developed Flask backend with RESTful APIs, integrated the AI chatbot system, and coordinated overall development of the application
Ayush Pratap Singh
Developed the Flask backend architecture, API endpoints, and integrated audio processing pipeline with the prediction engine
Ansh Srivastava
Designed and built the complete user interface with responsive layouts, interactive components, and modern styling
Anushka Singh
Analyzed the SceneFake dataset, performed data preprocessing, and assisted in model training and validation processes
Our Mission
To provide accessible, accurate, and reliable deepfake detection technology that protects individuals, organizations, and society from the harmful effects of synthetic media manipulation.
Accuracy
We strive for the highest detection accuracy through continuous research and model improvement.
Accessibility
Our tool is free and easy to use, making deepfake detection available to everyone.
Innovation
We leverage cutting-edge AI research to stay ahead of evolving deepfake technology.
Transparency
We believe in open communication about our methods, limitations, and results.
Our Technology
Our BiLSTM neural network is trained on the SceneFake dataset and uses MFCC feature extraction for robust deepfake detection.
Get In Touch
Have questions or want to collaborate? We'd love to hear from you!