Incorporating human-perceptual intelligence into model training has shown to
increase the generalization capability of models in several difficult biometric
tasks, such as presentation attack detection (PAD) and detection of synthetic
samples. After the initial collection phase, human visual saliency (e.g.,
eye-tracking data, or handwritten annotations) can be integrated into model
training through attention mechanisms, augmented training samples, or through
human perception-related components of loss functions. Despite their successes,
a vital, but seemingly neglected, aspect of any saliency-based training is the
level of salience granularity (e.g., bounding boxes, single saliency maps, or
saliency aggregated from multiple subjects) necessary to find a balance between
reaping the full benefits of human saliency and the cost of its collection. In
this paper, we explore several different levels of salience granularity and
demonstrate that increased generalization capabilities of PAD and synthetic
face detection can be achieved by using simple yet effective saliency
post-processing techniques across several different CNNs.

融入人类感知智能到模型训练能增强在难度较高的生物特征任务中的泛化能力，如攻击检测和合成样本检测。本文研究了不同的显著性粒度水平，通过使用简单但有效的显著性后处理技术，在数个卷积神经网络中实现了增强的攻击检测和合成人脸检测的泛化能力。

显著性的精细优化：基于显著性的生物特征攻击检测模型训练

Grains of Saliency: Optimizing Saliency-based Training of Biometric  Attack Detection Models

Deep learning-based models generalize better to unknown data samples after
being guided "where to look" by incorporating human perception into training
strategies. We made an observation that the entropy of the model's salience
trained in that way is lower when compared to salience entropy computed for
models training without human perceptual intelligence. Thus the question: does
further increase of model's focus, by lowering the entropy of model's class
activation map, help in further increasing the performance? In this paper we
propose and evaluate several entropy-based new loss function components
controlling the model's focus, covering the full range of the level of such
control, from none to its "aggressive" minimization. We show, using a problem
of synthetic face detection, that improving the model's focus, through lowering
entropy, leads to models that perform better in an open-set scenario, in which
the test samples are synthesized by unknown generative models. We also show
that optimal performance is obtained when the model's loss function blends
three aspects: regular classification, low-entropy of the model's focus, and
human-guided saliency.

本文探讨深度学习与人类感知相结合后，通过熵控制模型的焦点，优化模型的分类性能，提高在未知数据上的泛化能力，同时以人类引导视觉注意力为支撑的新损失函数作为控制手段，通过合理的损失函数组合，达到在合成样本检测方面的最优性能。