Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey

no code implementations • 8 Apr 2024 • Naveen Karunanayake, Ravin Gunawardena, Suranga Seneviratne, Sanjay Chawla

Finally, we highlight the limitations of the existing work and propose promising research directions that explore adversarial and OOD inputs within a unified framework.

Adversarial Robustness Anomaly Detection +1

ExCeL : Combined Extreme and Collective Logit Information for Enhancing Out-of-Distribution Detection

no code implementations • 23 Nov 2023 • Naveen Karunanayake, Suranga Seneviratne, Sanjay Chawla

Deep learning models often exhibit overconfidence in predicting out-of-distribution (OOD) data, underscoring the crucial role of OOD detection in ensuring reliability in predictions.

Out-of-Distribution Detection

Privacy-Preserving Spam Filtering using Functional Encryption

no code implementations • 8 Dec 2020 • Sicong Wang, Naveen Karunanayake, Tham Nguyen, Suranga Seneviratne

Spam classification over encrypted emails enables the classifier to classify spam email without accessing the email, hence protects the privacy of email content.

Classification General Classification +1

A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store

no code implementations • 2 Jun 2020 • Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume Jourjon

We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods.