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<b>Synthetic Realities and Data in Biometric Analysis and Security (SynRDinBAS)</b><br>
Workshop held at WACV 2025 (IEEE/CVF Winter Conference on Applications of Computer Vision) <br>
February 28 – March 4, 2024, Tucson, Arizona, US</div>
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<span style="color: rgb(70, 120, 134);"><u><a href="https://sites.google.com/view/synrdinbas-wacv2025" id="OWAbbca82e5-783f-b282-cff6-9e27372a6ed9" class="OWAAutoLink" style="color: rgb(70, 120, 134);">https://sites.google.com/view/synrdinbas-wacv2025</a></u></span><span style="color: rgb(0, 0, 0);">
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Paper submission:<b> December 12, 2024</b>, 11:59pm PST<br>
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<b>***</b> <b>Call for Papers ***</b> <br>
Recent advancements in generative models like GANs, VAEs, and diffusion models have transformed data-driven tasks in computer vision and AI by enabling the creation of highly realistic synthetic data. These models address data scarcity challenges, offering
versatile and ethical alternatives for training and testing machine learning algorithms. However, their realism also raises concerns, as synthetic data's indistinguishability from real data poses risks of misuse, manipulation, and potential harm when used
unethically. </div>
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Synthetic Realities and Data in Biometric Analysis and Security (<b>SynRDinBAS</b>)<b> </b>is a workshop that aims to explore the diverse applications of
<b><i>synthetic realities and data in biometric analysis</i></b>, while also addressing
<b><i>critical security issues</i></b> such as data privacy and ethical concerns of data manipulation. </div>
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Topics of interest include, but are not limited to:</div>
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<div>Novel generative models for synthesis of biometric data,</div>
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<div>Synthetic realities in immersive media for biometric interaction and analysis,</div>
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<div>Synthetic realities for behavioral biometric data collection and analysis,</div>
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<div>Label generation for synthetic data,</div>
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<div>Information leakage in synthetic data,</div>
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<div>Data factories for training biometric (detection, landmarking, recognition) models,</div>
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<div>Synthetic data for data augmentation,</div>
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<div>Data synthesis for bias mitigation and fairness,</div>
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<div>Quality assessment for synthetic data,</div>
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<div>Synthetic data for privacy protection,</div>
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<div>Novel applications of synthetic data,</div>
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<div>New synthetic datasets and performance benchmarks,</div>
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<div>Applications of synthetic data, e.g., deepfakes, virtual try-on, face and gesture editing,</div>
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<div>Partially or fully synthetically generated attacks on biometric systems for identification and verification,</div>
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<div>Detection of manipulated and synthetic content,</div>
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<div>Forensic analysis of synthetic data.</div>
</li></ul>
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<b>*** Submission ***</b> </div>
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<span style="color: rgb(0, 0, 0);">Submit your papers at: </span><span style="color: rgb(70, 120, 134);"><u><a href="https://cmt3.research.microsoft.com/SynRDinBAS2025" target="_blank" id="OWAf4ddf286-6cb5-0cca-3e9d-eceb44bbc133" class="OWAAutoLink" style="color: rgb(70, 120, 134);">https://cmt3.research.microsoft.com/SynRDinBAS2025</a></u></span></div>
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<b>*** Important Dates ***</b> <br>
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Full Paper Submission: December 12, 2024, 11:59pm PST <br>
Acceptance Notice: January 6, 2025, 11:59pm PST <br>
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<span style="color: rgb(0, 0, 0);">For more information, visit: </span><span style="color: rgb(70, 120, 134);"><u><a href="https://sites.google.com/view/synrdinbas-wacv2025" id="OWAeb3b2f18-50e9-8e0d-9b97-b1d2ac87f132" class="OWAAutoLink" style="color: rgb(70, 120, 134);">https://sites.google.com/view/synrdinbas-wacv2025</a></u></span></div>
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