About me

Hello, I am Jefferson Rodríguez, a Ph.D. student in Electronic and Computer Engineering at the PRA Lab, University of Cagliari, Italy. My research focuses on the development of advanced deep learning methodologies applied to biometrics and digital security. I am currently engaged in an innovative doctoral project that investigates the integration of watermarking and steganography techniques for facial images, aiming to improve secure facial image verification, authentication and proactive Deepfake detection.

Research Interests

  • Biometrics and Digital Security: Developing secure, resilient authentication/verification systems using state-of-the-art techniques based on biometric data.
  • Deep Learning and Image Processing: Applying neural networks and advanced algorithms to extract and analyze biometric features effectively.
  • Watermarking and Steganography: Innovating methods for embedding and concealing information within facial images to enhance data integrity and security.
  • Fraud Detection Systems: Designing and implementing robust systems for detecting and preventing fraudulent activities within digital environments.
  • Model Explainability: Investigating methods to improve the interpretability and transparency of deep learning models, ensuring that complex algorithms can be understood and trusted by end users.

Latest News


Publications at a glance

Cybersecurity publications
  • Currently working on Steganography & Watermarking ...
  • Currently working on Behavioral biometrics ...
Biomedical imaging publications
  • Kinematic motion representation in Cine-MRI to support cardiac disease classification, TCIV, 2022.
  • Deep learning representations to support COVID-19 diagnosis on CT-slices, Biomédica, 2021.
  • A Covid-19 Patient Severity Stratification using a 3D Convolutional Strategy on CT-Scans, ISBI, 2021.
  • Regional multiscale motion representation for cardiac disease prediction, STSIVA, 2019.
Vision-language publications: Sign language recognition
  • How important is motion in sign language translation?, IET Computer Vision, 2021.
  • Understanding Motion in Sign Language: A New Structured Translation Dataset, ACCV, 2020.
  • Towards on-line sign language recognition using cumulative SD-VLAD descriptors, CCC, 2018.
  • A kinematic gesture representation based on shape difference VLAD for sign language recognition, ICCVG, 2018.