Grains of Saliency: Optimizing Saliency-based Training of Biometric Attack Detection Models
arxiv(2024)
摘要
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.
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