470 research outputs found
A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification
For practical automatic speaker verification (ASV) systems, replay attack
poses a true risk. By replaying a pre-recorded speech signal of the genuine
speaker, ASV systems tend to be easily fooled. An effective replay detection
method is therefore highly desirable. In this study, we investigate a major
difficulty in replay detection: the over-fitting problem caused by variability
factors in speech signal. An F-ratio probing tool is proposed and three
variability factors are investigated using this tool: speaker identity, speech
content and playback & recording device. The analysis shows that device is the
most influential factor that contributes the highest over-fitting risk. A
frequency warping approach is studied to alleviate the over-fitting problem, as
verified on the ASV-spoof 2017 database
Detecting ADS-B Spoofing Attacks using Deep Neural Networks
The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key
component of the Next Generation Air Transportation System (NextGen) that
manages the increasingly congested airspace. It provides accurate aircraft
localization and efficient air traffic management and also improves the safety
of billions of current and future passengers. While the benefits of ADS-B are
well known, the lack of basic security measures like encryption and
authentication introduces various exploitable security vulnerabilities. One
practical threat is the ADS-B spoofing attack that targets the ADS-B ground
station, in which the ground-based or aircraft-based attacker manipulates the
International Civil Aviation Organization (ICAO) address (a unique identifier
for each aircraft) in the ADS-B messages to fake the appearance of non-existent
aircraft or masquerade as a trusted aircraft. As a result, this attack can
confuse the pilots or the air traffic control personnel and cause dangerous
maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network
(DNN)-based spoofing detector for ADS-B that consists of a message classifier
and an aircraft classifier. It allows a ground station to examine each incoming
message based on the PHY-layer features (e.g., IQ samples and phases) and flag
suspicious messages. Our experimental results show that SODA detects
ground-based spoofing attacks with a probability of 99.34%, while having a very
small false alarm rate (i.e., 0.43%). It outperforms other machine learning
techniques such as XGBoost, Logistic Regression, and Support Vector Machine. It
further identifies individual aircraft with an average F-score of 96.68% and an
accuracy of 96.66%, with a significant improvement over the state-of-the-art
detector.Comment: Accepted to IEEE CNS 201
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A Testbed for Developing and Evaluating GNSS Signal Authentication Techniques
An experimental testbed has been created for developing
and evaluating Global Navigation Satellite System (GNSS)
signal authentication techniques. The testbed advances the state
of the art in GNSS signal authentication by subjecting candidate
techniques to the strongest publicly-acknowledged GNSS spoofing
attacks. The testbed consists of a real-time phase-coherent GNSS
signal simulator that acts as spoofer, a real-time softwaredefined
GNSS receiver that plays the role of defender, and
post-processing versions of both the spoofer and defender. Two
recently-proposed authentication techniques are analytically and
experimentally evaluated: (1) a defense based on anomalous
received power in a GNSS band, and (2) a cryptographic
defense against estimation-and-replay-type spoofing attacks. The
evaluation reveals weaknesses in both techniques; nonetheless,
both significantly complicate a successful GNSS spoofing attackAerospace Engineering and Engineering Mechanic
Voice Spoofing Countermeasures: Taxonomy, State-of-the-art, experimental analysis of generalizability, open challenges, and the way forward
Malicious actors may seek to use different voice-spoofing attacks to fool ASV
systems and even use them for spreading misinformation. Various countermeasures
have been proposed to detect these spoofing attacks. Due to the extensive work
done on spoofing detection in automated speaker verification (ASV) systems in
the last 6-7 years, there is a need to classify the research and perform
qualitative and quantitative comparisons on state-of-the-art countermeasures.
Additionally, no existing survey paper has reviewed integrated solutions to
voice spoofing evaluation and speaker verification, adversarial/antiforensics
attacks on spoofing countermeasures, and ASV itself, or unified solutions to
detect multiple attacks using a single model. Further, no work has been done to
provide an apples-to-apples comparison of published countermeasures in order to
assess their generalizability by evaluating them across corpora. In this work,
we conduct a review of the literature on spoofing detection using hand-crafted
features, deep learning, end-to-end, and universal spoofing countermeasure
solutions to detect speech synthesis (SS), voice conversion (VC), and replay
attacks. Additionally, we also review integrated solutions to voice spoofing
evaluation and speaker verification, adversarial and anti-forensics attacks on
voice countermeasures, and ASV. The limitations and challenges of the existing
spoofing countermeasures are also presented. We report the performance of these
countermeasures on several datasets and evaluate them across corpora. For the
experiments, we employ the ASVspoof2019 and VSDC datasets along with GMM, SVM,
CNN, and CNN-GRU classifiers. (For reproduceability of the results, the code of
the test bed can be found in our GitHub Repository
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