A face morphing attack image can be verified to multiple identities, making
this attack a major vulnerability to processes based on identity verification,
such as border checks. Various methods have been proposed to detect face
morphing attacks, however, with low generalizability to unexpected
post-morphing processes. A major post-morphing process is the print and scan
operation performed in many countries when issuing a passport or identity
document. In this work, we address this generalization problem by adapting a
pixel-wise supervision approach where we train a network to classify each pixel
of the image into an attack or not, rather than only having one label for the
whole image. Our pixel-wise morphing attack detection (PW-MAD) solution proved
to perform more accurately than a set of established baselines. More
importantly, PW-MAD shows high generalizability in comparison to related works,
when evaluated on unknown re-digitized attacks. Additionally to our PW-MAD
approach, we create a new face morphing attack dataset with digital and
re-digitized samples, namely the LMA-DRD dataset that is publicly available for
research purposes upon request.

通过像素级别的监督方法，我们训练一个分类器来识别脸部变形攻击，我们的解决方案相比于现有算法更加准确，而且在检测重数读攻击时表现良好；另外，我们还创建了一个新的数据集，即 LMA-DRD 数据集，可用于研究目的。

PW-MAD：基于像素级别监督的通用人脸变形攻击检测

PW-MAD: Pixel-wise Supervision for Generalized Face Morphing Attack  Detection

Object detection methods are widely adopted for computer-aided diagnosis
using medical images. Anomalous findings are usually treated as objects that
are described by bounding boxes. Yet, many pathological findings, e.g., bone
fractures, cannot be clearly defined by bounding boxes, owing to considerable
instance, shape and boundary ambiguities. This makes bounding box annotations,
and their associated losses, highly ill-suited. In this work, we propose a new
bone fracture detection method for X-ray images, based on a labor effective and
flexible annotation scheme suitable for abnormal findings with no clear
object-level spatial extents or boundaries. Our method employs a simple,
intuitive, and informative point-based annotation protocol to mark localized
pathology information. To address the uncertainty in the fracture scales
annotated via point(s), we convert the annotations into pixel-wise supervision
that uses lower and upper bounds with positive, negative, and uncertain
regions. A novel Window Loss is subsequently proposed to only penalize the
predictions outside of the uncertain regions. Our method has been extensively
evaluated on 4410 pelvic X-ray images of unique patients. Experiments
demonstrate that our method outperforms previous state-of-the-art image
classification and object detection baselines by healthy margins, with an AUROC
of 0.983 and FROC score of 89.6%.

通过点标注信息将医学图像中的骨折病变检测方法进行有效优化，使检测能力准确提升。

X 光图像骨折检测和定位的新窗口损失函数 (基于点注释)

A New Window Loss Function for Bone Fracture Detection and Localization  in X-ray Images with Point-based Annotation

Face anti-spoofing (FAS) plays a vital role in securing face recognition
systems from the presentation attacks (PAs). As more and more realistic PAs
with novel types spring up, it is necessary to develop robust algorithms for
detecting unknown attacks even in unseen scenarios. However, deep models
supervised by traditional binary loss (e.g., `0' for bonafide vs. `1' for PAs)
are weak in describing intrinsic and discriminative spoofing patterns.
Recently, pixel-wise supervision has been proposed for the FAS task, intending
to provide more fine-grained pixel/patch-level cues. In this paper, we firstly
give a comprehensive review and analysis about the existing pixel-wise
supervision methods for FAS. Then we propose a novel pyramid supervision, which
guides deep models to learn both local details and global semantics from
multi-scale spatial context. Extensive experiments are performed on five FAS
benchmark datasets to show that, without bells and whistles, the proposed
pyramid supervision could not only improve the performance beyond existing
pixel-wise supervision frameworks, but also enhance the model's
interpretability (i.e., locating the patch-level positions of PAs more
reasonably). Furthermore, elaborate studies are conducted for exploring the
efficacy of different architecture configurations with two kinds of pixel-wise
supervisions (binary mask and depth map supervisions), which provides
inspirable insights for future architecture/supervision design.

本文综述分析了现有的人脸反欺诈像素级监督方法，提出了一种新的金字塔监督，在五个基准数据集上的实验验证了该监督能够提高性能并增强深度模型的可解释性。在不同架构配置和监督方法（二元掩码和深度图监督）的研究中，提供了启发性的见解，可用于未来的架构 / 监督设计。