56 research outputs found
Flow-Attention-based Spatio-Temporal Aggregation Network for 3D Mask Detection
Anti-spoofing detection has become a necessity for face recognition systems
due to the security threat posed by spoofing attacks. Despite great success in
traditional attacks, most deep-learning-based methods perform poorly in 3D
masks, which can highly simulate real faces in appearance and structure,
suffering generalizability insufficiency while focusing only on the spatial
domain with single frame input. This has been mitigated by the recent
introduction of a biomedical technology called rPPG (remote
photoplethysmography). However, rPPG-based methods are sensitive to noisy
interference and require at least one second (> 25 frames) of observation time,
which induces high computational overhead. To address these challenges, we
propose a novel 3D mask detection framework, called FASTEN
(Flow-Attention-based Spatio-Temporal aggrEgation Network). We tailor the
network for focusing more on fine-grained details in large movements, which can
eliminate redundant spatio-temporal feature interference and quickly capture
splicing traces of 3D masks in fewer frames. Our proposed network contains
three key modules: 1) a facial optical flow network to obtain non-RGB
inter-frame flow information; 2) flow attention to assign different
significance to each frame; 3) spatio-temporal aggregation to aggregate
high-level spatial features and temporal transition features. Through extensive
experiments, FASTEN only requires five frames of input and outperforms eight
competitors for both intra-dataset and cross-dataset evaluations in terms of
multiple detection metrics. Moreover, FASTEN has been deployed in real-world
mobile devices for practical 3D mask detection.Comment: 13 pages, 5 figures. Accepted to NeurIPS 202
Data Hiding with Deep Learning: A Survey Unifying Digital Watermarking and Steganography
Data hiding is the process of embedding information into a noise-tolerant
signal such as a piece of audio, video, or image. Digital watermarking is a
form of data hiding where identifying data is robustly embedded so that it can
resist tampering and be used to identify the original owners of the media.
Steganography, another form of data hiding, embeds data for the purpose of
secure and secret communication. This survey summarises recent developments in
deep learning techniques for data hiding for the purposes of watermarking and
steganography, categorising them based on model architectures and noise
injection methods. The objective functions, evaluation metrics, and datasets
used for training these data hiding models are comprehensively summarised.
Finally, we propose and discuss possible future directions for research into
deep data hiding techniques
SHAPFUZZ: Efficient Fuzzing via Shapley-Guided Byte Selection
Mutation-based fuzzing is popular and effective in discovering unseen code
and exposing bugs. However, only a few studies have concentrated on quantifying
the importance of input bytes, which refers to the degree to which a byte
contributes to the discovery of new code. They often focus on obtaining the
relationship between input bytes and path constraints, ignoring the fact that
not all constraint-related bytes can discover new code. In this paper, we
conduct Shapely analysis to understand the effect of byte positions on fuzzing
performance, and find that some byte positions contribute more than others and
this property often holds across seeds. Based on this observation, we propose a
novel fuzzing solution, ShapFuzz, to guide byte selection and mutation.
Specifically, ShapFuzz updates Shapley values (importance) of bytes when each
input is tested during fuzzing with a low overhead, and utilizes contextual
multi-armed bandit to trade off between mutating high Shapley value bytes and
low-frequently chosen bytes. We implement a prototype of this solution based on
AFL++, i.e., ShapFuzz. We evaluate ShapFuzz against ten state-of-the-art
fuzzers, including five byte schedule-reinforced fuzzers and five commonly used
fuzzers. Compared with byte schedule-reinforced fuzzers, ShapFuzz discovers
more edges and exposes more bugs than the best baseline on three different sets
of initial seeds. Compared with commonly used fuzzers, ShapFuzz exposes 20 more
bugs than the best comparison fuzzer, and discovers 6 more CVEs than the best
baseline on MAGMA. Furthermore, ShapFuzz discovers 11 new bugs on the latest
versions of programs, and 3 of them are confirmed by vendors
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