Deep learning, a robust framework for complex learning, outperforms previous machine learning approaches and finds widespread use. However, security vulnerabilities, especially in fusion in multi-color channel skin detection applications using adversarial machine learning (AML) and generative adversarial networks (GANs), lead to misclassifications. Researchers are actively exploring AML's and GANs' impact on misclassification, focusing on vulnerabilities in lighting conditions, skin-like patches in lesion segmentation, and insufficient data in facial emotion recognition. Yet, these areas only scratch the surface of potential AML vulnerabilities and GANs. To comprehensively address challenges, an in-depth investigation into AML and GANs components is crucial to uncover underlying reasons for misclassifying skin detection. This study addresses challenges of fusion in multi-color channel skin detection by creating a diverse dataset with 17M patches for enhanced feature fusion/training and meeting dataset criteria, investigating misclassifications using various deep learning models belonging to AML and GANs and color spaces (e.g., RGB, YCbCr, HSV, YUV), and exploring binary and multiclass scenarios. Notably, YCbCr outperformed RGB, achieving 98 % for binary skin classification, 84 % and 69 % for multiclass four and five-class scenarios. Binary classification for skin tones and their skin-like counterparts (e.g., black skin tone and black skin-like) yielded 97 %, 81 %, 60 %, and 51 % for black, brown, medium, and fair, respectively. Exploration of darker skin tones showed improved accuracy. Benchmarking with a CNN and RNN hybrid achieved 99 % accuracy, surpassing the initial 91 %, while SAE reached 97 %. The study explores implications of overlapping between skin and skin-tone recognition, offering insights for developing a generalized skin detector. The investigation demonstrates that improper color space selection can make lighting conditions exploitable in AML attacks and GANs, emphasizing the crucial role of color space choice in mitigating vulnerabilities.

中文翻译：

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探索对抗性深度学习在多颜色通道皮肤检测应用中的融合


深度学习是复杂学习的强大框架，其性能优于以前的机器学习方法并得到广泛应用。然而，安全漏洞，特别是在使用对抗性机器学习（AML）和生成对抗性网络（GAN）的多颜色通道皮肤检测应用程序的融合中，会导致错误分类。研究人员正在积极探索 AML 和 GAN 对错误分类的影响，重点关注光照条件下的漏洞、病变分割中的皮肤样斑块以及面部情绪识别中的数据不足。然而，这些领域仅仅触及了潜在的 AML 漏洞和 GAN 的表面。为了全面应对挑战，深入研究 AML 和 GAN 组件对于揭示皮肤检测错误分类的根本原因至关重要。本研究通过创建具有 1700 万个补丁的多样化数据集来解决多颜色通道皮肤检测中的融合挑战，以增强特征融合/训练并满足数据集标准，使用属于 AML 和 GAN 以及颜色空间的各种深度学习模型（例如， RGB、YCbCr、HSV、YUV），并探索二进制和多类场景。值得注意的是，YCbCr 的性能优于 RGB，在二元皮肤分类中实现了 98%，在多类四类和五类场景中实现了 84% 和 69%。肤色及其类似皮肤对应物（例如，黑色肤色和类似黑色皮肤）的二元分类对于黑色、棕色、中等和白皙分别产生 97%、81%、60% 和 51%。对较深肤色的探索显示出准确性的提高。使用 CNN 和 RNN 混合进行基准测试，准确率达到了 99%，超过了最初的 91%，而 SAE 达到了 97%。 该研究探讨了皮肤和肤色识别之间重叠的影响，为开发通用皮肤检测器提供了见解。调查表明，不正确的色彩空间选择可能会使照明条件在 AML 攻击和 GAN 中被利用，这强调了色彩空间选择在缓解漏洞方面的关键作用。