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From the 1 of 8 linked papers with an AI index.

collaborators

8 papers

cs.CV2026

Have I Seen You? Embedding Behavior Signals Synthetic Face Dataset Membership

Paweł Borsukiewicz, Daniele Lunghi, Wendkûuni C. Ouédraogo +2

Synthetic face datasets are increasingly used to reduce privacy exposure and data access constraints in biometric recognition. Yet the generators that produce these datasets are tr…

cs.SE2026

Semantic Drift in Bug Resolution: How Behavioral Signals Propagate from Reports to Tests and Patches

Wendkûuni C. Ouédraogo, Wendkûuni C. Ouédraogo, Yinghua Li +10

Desc2Fix is a framework for measuring semantic alignment between bug reports, triggering tests, and developer-written fixes. Alignment is operationalized through structured behavio…

cs.CV2026

Benchmarking Face Recognition without Real Faces

Paweł Borsukiewicz, Daniele Lunghi, Wendkûuni C. Ouédraogo +2

The paper evaluates whether synthetic face datasets can replace real‑face benchmarks for assessing face recognition models, finding that the best synthetic sets achieve comparable…

cs.SE2026

Humanizing Automatically Generated Unit Test Suites with LLM-Based Refactoring

Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang +7

Search-based test generation tools such as EvoSuite produce compilable and high-coverage unit tests at scale, but their suites are often hard to read and maintain. LLMs can generat…

cs.CV2026

Adversarial Camouflage

Paweł Borsukiewicz, Daniele Lunghi, Melissa Tessa +2

While the rapid development of facial recognition algorithms has enabled numerous beneficial applications, their widespread deployment has raised significant concerns about the ris…

cs.CV2025

Beyond Real Faces: Synthetic Datasets Can Achieve Reliable Recognition Performance without Privacy Compromise

Paweł Borsukiewicz, Fadi Boutros, Iyiola E. Olatunji +4

The deployment of facial recognition systems has created an ethical dilemma: achieving high accuracy requires massive datasets of real faces collected without consent, leading to d…