This paper describes the results of the 2023 edition of the ''LivDet'' series
of iris presentation attack detection (PAD) competitions. New elements in this
fifth competition include (1) GAN-generated iris images as a category of
presentation attack instruments (PAI), and (2) an evaluation of human accuracy
at detecting PAI as a reference benchmark. Clarkson University and the
University of Notre Dame contributed image datasets for the competition,
composed of samples representing seven different PAI categories, as well as
baseline PAD algorithms. Fraunhofer IGD, Beijing University of Civil
Engineering and Architecture, and Hochschule Darmstadt contributed results for
a total of eight PAD algorithms to the competition. Accuracy results are
analyzed by different PAI types, and compared to human accuracy. Overall, the
Fraunhofer IGD algorithm, using an attention-based pixel-wise binary
supervision network, showed the best-weighted accuracy results (average
classification error rate of 37.31%), while the Beijing University of Civil
Engineering and Architecture's algorithm won when equal weights for each PAI
were given (average classification rate of 22.15%). These results suggest that
iris PAD is still a challenging problem.

2023 年的 ''LivDet'' 系列虹膜展示攻击检测（PAD）竞赛的结果。新的元素包括生成对抗网络（GAN）产生的虹膜图像作为展示攻击工具的一类，以及评估人类检测展示攻击工具的准确性作为参考基准。结果分析了不同类型的展示攻击工具的准确性，并与人类准确性进行了比较。总体而言，Fraunhofer IGD 算法表现出最佳的加权准确性结果（平均分类错误率为 37.31%），而北京土木工程与建筑大学的算法在每个展示攻击工具给予相等权重时获胜（平均分类率为 22.15%）。这些结果表明虹膜 PAD 仍然是一个具有挑战性的问题。

虹膜活体检测竞赛 (LivDet-Iris) -- 2023 年版

Iris Liveness Detection Competition (LivDet-Iris) -- The 2023 Edition

Bowers and colleagues argue that DNNs are poor models of biological vision
because they often learn to rival human accuracy by relying on strategies that
differ markedly from those of humans. We show that this problem is worsening as
DNNs are becoming larger-scale and increasingly more accurate, and prescribe
methods for building DNNs that can reliably model biological vision.

DNNs 对生物视觉的模拟能力不佳，因其依靠与人类截然不同的策略实现与人类准确度相匹敌的结果。随着 DNNs 规模的增大与准确度的提高，这一问题变得更加严重。本研究提出了一种可靠地模拟生物视觉的 DNN 构建方法。

修复深度神经网络的问题需要更好的训练数据和学习算法

Fixing the problems of deep neural networks will require better training  data and learning algorithms

We present a randomized controlled trial for a model-in-the-loop regression
task, with the goal of measuring the extent to which (1) good explanations of
model predictions increase human accuracy, and (2) faulty explanations decrease
human trust in the model. We study explanations based on visual saliency in an
image-based age prediction task for which humans and learned models are
individually capable but not highly proficient and frequently disagree. Our
experimental design separates model quality from explanation quality, and makes
it possible to compare treatments involving a variety of explanations of
varying levels of quality. We find that presenting model predictions improves
human accuracy. However, visual explanations of various kinds fail to
significantly alter human accuracy or trust in the model - regardless of
whether explanations characterize an accurate model, an inaccurate one, or are
generated randomly and independently of the input image. These findings suggest
the need for greater evaluation of explanations in downstream decision making
tasks, better design-based tools for presenting explanations to users, and
better approaches for generating explanations.

本试验的目的是研究在模型与人类在图像年龄预测任务中表现相似情况下，给人类提供好的模型预测解释是否会提高人类预测的准确度，而不好的解释会降低人类对模型的信任。研究发现，给人类提供模型预测能提高人类准确度，但图像解释并不能改变人类对模型的准确度或信任的显著差异。这一发现表明有必要对后续的决策任务进行更全面的解释评估，为用户提供基于设计的解释工具，并思考更好的生成解释的方法。