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From the 1 of 11 linked papers with an AI index.

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11 papers

cs.CV2026

Are DeepFakes Realistic Enough? Exploring Semantic Mismatch as a Novel Challenge

Sharayu Nilesh Deshmukh, Kailash A. Hambarde, Joana C. Costa +2

The paper introduces a new evaluation class for DeepFake detection that captures semantic inconsistencies between audio and video (RARV‑SMM) and tests how current models handle suc…

cs.CV2026

Generalization Under Scrutiny: Cross-Domain Detection Progresses, Pitfalls, and Persistent Challenges

Saniya M. Deshmukh, Kailash A. Hambarde, Hugo Proença

Object detection models trained on a source domain often exhibit significant performance degradation when deployed in unseen target domains, due to various kinds of variations, suc…

cs.CV2026

The Good, the Better, and the Best: Improving the Discriminability of Face Embeddings through Attribute-aware Learning

Ana Dias, João Ribeiro Pinto, Hugo Proença +1

Despite recent advances in face recognition, robust performance remains challenging under large variations in age, pose, and occlusion. A common strategy to address these issues is…

cs.CV2026

SRL-MAD: Structured Residual Latents for One-Class Morphing Attack Detection

Diogo J. Paulo, Hugo Proença, João C. Neves

Face morphing attacks represent a significant threat to biometric systems as they allow multiple identities to be combined into a single face. While supervised morphing attack dete…

cs.CV2026

Rectifying Geometry-Induced Similarity Distortions for Real-World Aerial-Ground Person Re-Identification

Kailash A. Hambarde, Hugo Proença

Aerial-ground person re-identification (AG-ReID) is fundamentally challenged by extreme viewpoint and distance discrepancies between aerial and ground cameras, which induce severe…

cs.CV2026

FD-MAD: Frequency-Domain Residual Analysis for Face Morphing Attack Detection

Diogo J. Paulo, Hugo Proença, João C. Neves

Face morphing attacks present a significant threat to face recognition systems used in electronic identity enrolment and border control, particularly in single-image morphing attac…