90 research outputs found
Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems
Due to its convenience, biometric authentication, especial face
authentication, has become increasingly mainstream and thus is now a prime
target for attackers. Presentation attacks and face morphing are typical types
of attack. Previous research has shown that finger-vein- and fingerprint-based
authentication methods are susceptible to wolf attacks, in which a wolf sample
matches many enrolled user templates. In this work, we demonstrated that wolf
(generic) faces, which we call "master faces," can also compromise face
recognition systems and that the master face concept can be generalized in some
cases. Motivated by recent similar work in the fingerprint domain, we generated
high-quality master faces by using the state-of-the-art face generator StyleGAN
in a process called latent variable evolution. Experiments demonstrated that
even attackers with limited resources using only pre-trained models available
on the Internet can initiate master face attacks. The results, in addition to
demonstrating performance from the attacker's point of view, can also be used
to clarify and improve the performance of face recognition systems and harden
face authentication systems.Comment: Accepted to be Published in Proceedings of the 2020 International
Joint Conference on Biometrics (IJCB 2020), Houston, US
Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos
Detecting manipulated images and videos is an important topic in digital
media forensics. Most detection methods use binary classification to determine
the probability of a query being manipulated. Another important topic is
locating manipulated regions (i.e., performing segmentation), which are mostly
created by three commonly used attacks: removal, copy-move, and splicing. We
have designed a convolutional neural network that uses the multi-task learning
approach to simultaneously detect manipulated images and videos and locate the
manipulated regions for each query. Information gained by performing one task
is shared with the other task and thereby enhance the performance of both
tasks. A semi-supervised learning approach is used to improve the network's
generability. The network includes an encoder and a Y-shaped decoder.
Activation of the encoded features is used for the binary classification. The
output of one branch of the decoder is used for segmenting the manipulated
regions while that of the other branch is used for reconstructing the input,
which helps improve overall performance. Experiments using the FaceForensics
and FaceForensics++ databases demonstrated the network's effectiveness against
facial reenactment attacks and face swapping attacks as well as its ability to
deal with the mismatch condition for previously seen attacks. Moreover,
fine-tuning using just a small amount of data enables the network to deal with
unseen attacks.Comment: Accepted to be Published in Proceedings of the IEEE International
Conference on Biometrics: Theory, Applications and Systems (BTAS) 2019,
Florida, US
Capsule-forensics: Using Capsule Networks to Detect Forged Images and Videos
Recent advances in media generation techniques have made it easier for
attackers to create forged images and videos. State-of-the-art methods enable
the real-time creation of a forged version of a single video obtained from a
social network. Although numerous methods have been developed for detecting
forged images and videos, they are generally targeted at certain domains and
quickly become obsolete as new kinds of attacks appear. The method introduced
in this paper uses a capsule network to detect various kinds of spoofs, from
replay attacks using printed images or recorded videos to computer-generated
videos using deep convolutional neural networks. It extends the application of
capsule networks beyond their original intention to the solving of inverse
graphics problems
Identifying Computer-Translated Paragraphs using Coherence Features
We have developed a method for extracting the coherence features from a
paragraph by matching similar words in its sentences. We conducted an
experiment with a parallel German corpus containing 2000 human-created and 2000
machine-translated paragraphs. The result showed that our method achieved the
best performance (accuracy = 72.3%, equal error rate = 29.8%) when it is
compared with previous methods on various computer-generated text including
translation and paper generation (best accuracy = 67.9%, equal error rate =
32.0%). Experiments on Dutch, another rich resource language, and a low
resource one (Japanese) attained similar performances. It demonstrated the
efficiency of the coherence features at distinguishing computer-translated from
human-created paragraphs on diverse languages.Comment: 9 pages, PACLIC 201
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