论文标题
cit-gan：循环图像翻译生成对抗网络，并在虹膜演示攻击检测中应用
CIT-GAN: Cyclic Image Translation Generative Adversarial Network With Application in Iris Presentation Attack Detection
论文作者
论文摘要
在这项工作中，我们提出了一种新型的循环图像翻译生成对抗网络（CIT-GAN），用于多域样式转移。为了促进这一点，我们引入了一个样式网络，该网络具有学习培训数据集中每个域的样式特征的能力。样式网络可帮助生成器将图像从源域转换为参考域，并生成具有参考域样式特征的合成图像。每个领域的学习风格特征都取决于样式损失和域分类损失。这会导致每个域内样式特征的变化。提出的citgan用于虹膜表现攻击检测（PAD）的背景下，以生成训练集中代表性不足的类的合成呈现攻击（PA）样本。使用当前的最新虹膜垫方法进行的评估证明了将这种合成生成的PA样品用于训练PAD方法的功效。此外，使用特里切特（Frechet Inception）距离（FID）得分评估合成生成的样品的质量。结果表明，所提出的方法产生的合成图像的质量优于包括Stargan在内的其他竞争方法的质量。
In this work, we propose a novel Cyclic Image Translation Generative Adversarial Network (CIT-GAN) for multi-domain style transfer. To facilitate this, we introduce a Styling Network that has the capability to learn style characteristics of each domain represented in the training dataset. The Styling Network helps the generator to drive the translation of images from a source domain to a reference domain and generate synthetic images with style characteristics of the reference domain. The learned style characteristics for each domain depend on both the style loss and domain classification loss. This induces variability in style characteristics within each domain. The proposed CIT-GAN is used in the context of iris presentation attack detection (PAD) to generate synthetic presentation attack (PA) samples for classes that are under-represented in the training set. Evaluation using current state-of-the-art iris PAD methods demonstrates the efficacy of using such synthetically generated PA samples for training PAD methods. Further, the quality of the synthetically generated samples is evaluated using Frechet Inception Distance (FID) score. Results show that the quality of synthetic images generated by the proposed method is superior to that of other competing methods, including StarGan.
