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Jeremy Dawson(西弗吉尼亚大学):CITeR 高质量变形数据集.pdf

上传人: 芦苇 编号:651658 2025-05-01 15页 2.36MB

1、Center for Identification Technology ResearchCITeRCITeR High-Quality Morph DatasetsNasser Nasrabadi(JHU),Jeremy Dawson(WVU),Chen Liu(CU),Stephanie Schuckers(CU),David Doermann(UB),Srirangaraj Setlur(UB),Siwei Lyu(UB)Center for Identification Technology ResearchCITeR2ProblemThere is a lack of high-qu

2、ality morph face image datasets that can be used to train detectors,evaluate performance,etc.Research Goals:To generate high-quality sequestered face morphing datasets(including manually check/touchup,print&scan,compressed,perturbed)that can benefit and serve as the milestone for developing defense

3、mechanisms against face morphing attacks(NIST FRVT Morph Detection Report,06/202023,https:/pages.nist.gov/frvt/html/frvt_morph.html)Generate a high-quality synthetic face database for morph face generation.Why is this important?Single and differential morph detectors are used to identify morphed fac

4、es during the passport applications and at the border-crossing for entry and exit scenario.2Center for Identification Technology ResearchCITeR3Our ApproachGenerate low-quality to high-quality morph images.Assemble several databases with different image resolutions to represent the faces captured dur

5、ing border crossing,webcam,and high-quality mugshot passport photos.Generate morph images using WVU,CU,and BU advanced morphing techniques(e.g.,combined land-mark and StyleGAN-based morphing,manipulating StyleGAN-based latent codes for morphing,transformer-based and diffusion-based morphing techniqu

6、es.Generate print&scan(P&S),compressed,perturbed morph images.Use several commercial printer&scanners to generate different quality P&S morph images.Generate compressed,adversarially perturbed,and manually touched up morph images.Evaluate the visual and quantitative quality of morph images.Generate

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本文介绍了CITeR团队开发的高质量变脸图像数据集,旨在用于训练检测器和评估性能。研究目标包括生成高质量序列化的面部变脸数据集,并作为防御机制的里程碑。方法包括使用不同的图像分辨率代表不同情境下的面部捕获,并利用先进的变脸技术生成图像。 主要成就包括: 1. WVU通过 wavelet分解和扩散自动编码器生成高质量变脸图像。 2. CU开发了Fast-DiM和Greedy-DiM算法,显著降低了计算复杂度,并在评估数据上取得了最优性能。 3. UB基于ArcFace相似度度量生成高质量StyleGAN2变脸图像,并在不同数据集上评估了性能。 此外,文章还提到了人工修补变脸图像以去除瑕疵,并建立了评价变脸图像质量的数据集。这些成果对于提高变脸攻击的检测和防御具有重要意义。
"如何提高面部变造图像的质量?" "面部变造技术在安全领域有哪些应用?" "如何评估面部变造攻击的检测性能?"
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