With the advent of social media, fun selfie filters have come into tremendous
mainstream use affecting the functioning of facial biometric systems as well as
image recognition systems. These filters vary from beautification filters and
Augmented Reality (AR)-based filters to filters that modify facial landmarks.
Hence, there is a need to assess the impact of such filters on the performance
of existing face recognition systems. The limitation associated with existing
solutions is that these solutions focus more on the beautification filters.
However, the current AR-based filters and filters which distort facial key
points are in vogue recently and make the faces highly unrecognizable even to
the naked eye. Also, the filters considered are mostly obsolete with limited
variations. To mitigate these limitations, we aim to perform a holistic impact
analysis of the latest filters and propose an user recognition model with the
filtered images. We have utilized a benchmark dataset for baseline images, and
applied the latest filters over them to generate a beautified/filtered dataset.
Next, we have introduced a model FaceFilterNet for beautified user recognition.
In this framework, we also utilize our model to comment on various attributes
of the person including age, gender, and ethnicity. In addition, we have also
presented a filter-wise impact analysis on face recognition, age estimation,
gender, and ethnicity prediction. The proposed method affirms the efficacy of
our dataset with an accuracy of 87.25% and an optimal accuracy for facial
attribute analysis.

社交媒体上流行的自拍滤镜对面部生物识别系统和图像识别系统的功能产生了巨大影响，本研究旨在综合评估最新滤镜对现有人脸识别系统性能的影响，并提出了一种基于滤镜图像的用户识别模型。

FaceFilterSense：一种抵抗滤镜的面部识别和面部属性分析框架

FaceFilterSense: A Filter-Resistant Face Recognition and Facial  Attribute Analysis Framework

Deep neural networks have recently achieved tremendous success in image
classification. Recent studies have however shown that they are easily misled
into incorrect classification decisions by adversarial examples. Adversaries
can even craft attacks by querying the model in black-box settings, where no
information about the model is released except its final decision. Such
decision-based attacks usually require lots of queries, while real-world image
recognition systems might actually restrict the number of queries. In this
paper, we propose qFool, a novel decision-based attack algorithm that can
generate adversarial examples using a small number of queries. The qFool method
can drastically reduce the number of queries compared to previous
decision-based attacks while reaching the same quality of adversarial examples.
We also enhance our method by constraining adversarial perturbations in
low-frequency subspace, which can make qFool even more computationally
efficient. Altogether, we manage to fool commercial image recognition systems
with a small number of queries, which demonstrates the actual effectiveness of
our new algorithm in practice.

该研究提出了 qFool 算法，它是一种基于决策的攻击算法，可以使用少量的查询生成对抗样本。该算法可以在减少查询数量的同时，在低频子空间中限制对抗扰动，从而使其更具计算效率。最终，该算法在商业图像识别系统中获得了欺骗效果的证明。