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Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Target tracking in multistatic radar systems is fundamental to wide-area surveillance, yet it faces two major challenges. First, the measurement geometry is highly nonlinear and varies with target ran

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Perceptual image hashing aims to generate compact representations that are robust to content-preserving distortions. This letter presents a deep learning-based framework that learns invariant hash cod

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

This paper primarily considers the robust estimation problem under Wasserstein distance constraints on the parameter and noise distributions in the linear measurement model with additive noise, which

Section: Publications & Resources

Publication: IEEE Transactions on Image Processing

Adversarial training, one of the most effective methods for enhancing neural network robustness, is typically formulated as a min-max game between an attacker and a defender. Despite its success, most

Section: Events

Date:  6-October-2026 Time: 11:00 AM ET (New York Time) Presenter: Dr. Ricardo Bianchini Website link:  Register

From adversarial ML to cryptography, uncover the latest security breakthroughs at WIFS 2026. Join us in Sendai, Japan this December to connect with global experts and shape the future of digital forensics!

Don't Miss Out: Deadline Extended to 2 October 2026 for Awards Board, Industry Board, and Nominations & Elections Committee Positions

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

This letter addresses target detection for intra-pulse polarization-coding radar, where conventional fixed-polarization detectors cannot fully exploit the polarization variation within a pulse. A pola

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Stereonetworks trained on synthetic data often degrade in real-world scenes. Within an IGEV-style pipeline, domain-sensitive features may disturb correspondence cues, average matching-dimension poolin

Section: Publications & Resources

Publication: IEEE Signal Processing Letters

Multi-scale graph neural networks have shown strong performance in multivariate time-series forecasting by integrating representations at different temporal resolutions. However, existing fusion mecha