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About 3,119 papers for “Spoofing attack”

Spoofing attack of drone

Zhang, Renyu · DR-NTU (Nanyang Technological University) · 2018

Nowadays, the increasing application of drones makes our lives more and more convenient. Various tasks such as: surveillance, emergency rescue, aerial photography, etc. can be accomplished by drones now. In China, some express companies have begun to use drones to do the deliveries. There are two main ways to navigate a drone. The first way is to control drones through a remote controller. As a person is needed, sometimes, it is not a very profitable way. The other way is setting the destination for drones, and it will get to the destination automatically with the help of positioning services. This way can reduce the labor cost as the drones travel automatically. In preset, most positioning services is provided by global navigation satellite systems (GNSS). The global positioning system (GPS) is the most widely used GNSS. But it is known that civilian GPS signals are vulnerable to many kinds of attacks. In all of these attacks, the GPS spoofing is the most threatening one. The spoofing attack can mislead the victim into thinking it is in a position which is different from its real position. As the application demand of drones is increasing, the drones are likely to be victims of the spoofing attack.
\nThe 5th generation wireless systems, abbreviated 5G, is believed to be able to provide high quality and low delay positioning services with its features like ultra-dense networks, MIMO technology, device to device communications, etc. The observed time difference of arrival (OTDOA) positioning, which has been introduced to long-term evolution network, is a promising way to provide positioning services.
\nIn this work, the possibility of the spoofing attack on several drones which are keep in touch with each other and know each other relative positions is studied. The possible spoofed location with different number of spoofing signals is investigated. The feasibility study of spoofing attack on an OTDOA navigated drone and the simulation of it are included. Finally, some countermeasures against these attacks are proposed.

Cited by 5

Backdoor Poisoning Attack Against Face Spoofing Attack Detection Methods

Shota Iwamatsu, Koichi Ito, T. Aoki · ArXiv.org · 2025

Face recognition systems are robust against environmental changes and noise, and thus may be vulnerable to illegal authentication attempts using user face photos, such as spoofing attacks. To prevent such spoofing attacks, it is crucial to discriminate whether the input image is a live user image or a spoofed image prior to the face recognition process. Most existing spoofing attack detection methods utilize deep learning, which necessitates a substantial amount of training data. Consequently, if malicious data is injected into a portion of the training dataset, a specific spoofing attack may be erroneously classified as live, leading to false positives. In this paper, we propose a novel backdoor poisoning attack method to demonstrate the latent threat of backdoor poisoning within face anti-spoofing detection. The proposed method enables certain spoofing attacks to bypass detection by embedding features extracted from the spoofing attack's face image into a live face image without inducing any perceptible visual alterations. Through experiments conducted on public datasets, we demonstrate that the proposed method constitutes a realistic threat to existing spoofing attack detection systems.

Cited by 0Open access

Automatic Speaker Verification Under Spoofing Attack

Soumya Priyadarsini Panda, Krishna Pratap Singh · 2021

Speaker verification involves verifying the identity of a speaker based on the spoken utterances. Speech carries different information about the speaker, such as linguistic content, emotion, the acoustic environment, etc, which are considered as major factors for identifying a speaker. However, the reliability of such systems these days is compromised as a result of spoofing attacks, where fraudsters can claim the identity of some authorized person to gain access to protected resources. They may present specially crafted speech signals that reflect the characteristics of an enrolled speaker's voice. This may lead to a high increase in the false acceptance rate and may affect the overall performance of those systems. In this paper, we present a robust speaker verification system that is capable of analyzing the speech feature variations in case of voice spoofing attacks. The presented model uses the Gaussian Mixture Model (GMM) and is trained on varied voice samples with genuine and spoofed voice samples. Experiments were performed to analyze the performance of the model under genuine as well as spoofing attack scenarios and are also compared with the Dynamic Time Warping (DTW) ASV model. The obtained results in all the experiments show the efficiency of the presented technique in detecting spoofed voice samples and resulted in reducing the false acceptance rate hence enhancing the overall performance of the system.

Cited by 0

Quickest detection of GPS spoofing attack

Zhenghao Zhang, Matthew Trinkle, Lijun Qian et al. · 2012

GPS is being increasingly used in critical infrastructures in modern society. Therefore, the reliability of GPS is extremely important. Recently, the vulnerability of GPS to a spoofing attack has been discussed in the literature. A malicious attacker may spoof a GPS receiver, causing it to provide incorrect navigation and timing information, which may lead to serious damage. Therefore, it is extremely important to detect spoofing attacks as quickly as possible. In this paper, we propose a quickest spoofing detection algorithm to combat GPS spoofing attacks. The experimental results demonstrate that the proposed algorithm can effectively detect the spoofing attack quickly after it occurs.

Cited by 29

Backdoor Poisoning Attack Against Face Spoofing Attack Detection Methods

Shota Iwamatsu, Koichi Ito, Takafumi Aoki · 2025

Face recognition systems are robust against environmental changes and noise, and thus may be vulnerable to illegal authentication attempts using user face photos, such as spoofing attacks. To prevent such spoofing attacks, it is crucial to discriminate whether the input image is a live user image or a spoofed image prior to the face recognition process. Most existing spoofing attack detection methods utilize deep learning, which necessitates a substantial amount of training data. Consequently, if malicious data is injected into a portion of the training dataset, a specific spoofing attack may be erroneously classified as live, leading to false positives. In this paper, we propose a novel backdoor poisoning attack method to demonstrate the latent threat of backdoor poisoning within face anti-spoofing detection. The proposed method enables certain spoofing attacks to bypass detection by embedding features extracted from the spoofing attack's face image into a live face image without inducing any perceptible visual alterations. Through experiments conducted on public datasets, we demonstrate that the proposed method constitutes a realistic threat to existing spoofing attack detection systems.

Cited by 0

Spoofing Attack Detection by Anomaly Detection

Soroush Fatemifar, Shervin Rahimzadeh Arashloo, Muhammad Awais et al. · 2019

Spoofing attacks on biometric systems can seriously compromise their practical utility. In this paper we focus on face spoofing detection. The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task, attempting to separate normal accesses from samples of different types of spoofing attacks. In this paper we adopt the anomaly detection approach proposed in [1], where the detector is trained on genuine accesses only using one-class classifiers and investigate the merit of subject specific solutions. We show experimentally that subject specific models are superior to the commonly used client independent method. We also demonstrate that the proposed approach is more robust than multiclass formulations to unseen attacks.

Cited by 40

Attack-Agnostic Deep Face Anti-Spoofing

Ajian Liu, Zichang Tan, Yanyan Liang et al. · 2023

The task of face anti-spoofing (FAS) is to determine whether the captured face from a face recognition system is live or fake. Current methods which are trained with existing fake faces ignore the generalization and perform poorly on unseen attacks. To tackle this problem, a novel Attack-agnostic Face Anti-spoofing framework is proposed. Different from previous methods that can be treated as a defense system, we regard face anti-spoofing as a unified framework with the attack and defense systems and optimize the defense system against unseen attacks via adversarial training with the attack system. Concretely, the attack system consists of two modules: an Adversarial learning-based Attack Pattern Generation (Adv-APG) module and a Supervised learning-based Attack Pattern Drift (Sup-APD) module. The Adv-APG module generates a series of spoofing samples by recombining a live face with known attack patterns in a generative way. The Sup-APD module pulls the generated spoofing samples in a supervised way to an unknown domain that makes the defense system ineffective. The defense system is free to choose and compatible with our attack system. Extensive experiments are conducted by using three different defense architectures to verify that the proposed attack system can improve the performance on both seen- and unseen attacks on multiple datasets.

Cited by 20

Spoofing Attack Augmentation: Can Differently-Trained Attack Models Improve Generalisation?

Wanying Ge, Xin Wang, Junichi Yamagishi et al. · 2024

A reliable deepfake detector or spoofing countermeasure (CM) should be robust in the face of unpredictable spoofing attacks. To encourage the learning of more generaliseable artefacts, rather than those specific only to known attacks, CMs are usually exposed to a broad variety of different attacks during training. Even so, the performance of deeplearning-based CM solutions are known to vary, sometimes substantially, when they are retrained with different initialisations, hyper-parameters or training data partitions. We show in this paper that the potency of spoofing attacks, also deep-learning-based, can similarly vary according to training conditions, sometimes resulting in substantial degradations to detection performance. Nevertheless, while a RawNet2 CM model is vulnerable when only modest adjustments are made to the attack algorithm, those based upon graph attention networks and self-supervised learning are reassuringly robust. The focus upon training data generated with different attack algorithms might not be sufficient on its own to ensure generaliability; some form of spoofing attack augmentation at the algorithm level can be complementary.

Cited by 4

GPS replay spoofing attack - delay line assumptions

Daniel Pospíšil, Jiří Veselý · 2025

The world conflicts of the $21^{\text {st }}$ century and the reliance of military success on available position and time are defining factors for every new conflict. One of the last conflicts in Ukraine raised the importance of GNSS, its robustness, and reliance. We have examined very carefully the current state of knowledge in GNSS robustness with a focus on GPS. This research paper is stepping-stone on a fully controlled array of high-gain antennas capable of optional delay and replay spoofing attack without the need of decryption of protected GPS signal. The paper begins with a brief description of Space Vehicles positions and their observability by means of modelling of space vehicle position for a desired time window, describing power relations with a common electronic spare part. The observability of SVs was examined with real data with output as a need between intervals to steer antennas. The output of this paper is a short guide to achieve successful replay spoofing attack with a generic antennas and coaxial cable as a delay line. The results of this paper allow us to prove the concept with low-budget components.

Cited by 0

Efficient Speaker Verification System with Spoofing Attack

Malathi Sharavanan · 2014

In the existing system the state-of-the-art speaker verification system cannot distinguish the natural speech and converted speech. But in this project an attempt will be made to build an efficient speaker verification system to detect natural speech and synthesized speech. In this paper, we present new results evaluating the current state-of-the-art speaker verification system, Gaussian mixture model supervector with joint factor analysis (GMM-ZJFA) system, against spoofing attacks. In this the spoofing attacks are simulated by Gaussian mixture model based voice conversion technique. The results show that GMM-based conversion method which increases the false acceptance rate (FAR) from 3.24% to 17.33%. This suggests that GMM-JFA system is less vulnerable towards GMM-based conversion. The software used over here is matlab.

Cited by 0

The SSL MIMT Attack with DNS Spoofing

Ya Lan Zhang, Gui Shu Xia · Applied Mechanics and Materials · 2013

SSL protocol is widely used in network security, such as Internet user account, electronic exchange data, etc. The SSLStrip MITM attack exist the problem of processing performance and intrusion detection. Based on the idea of network Phishing, the SSL MITM attack with the DNS spoofing resolved the above mentioned problems. The security of SSL login in daily Internet services was also analyzed, and the improvement and suggestion were provided.

Cited by 4

Spoofing speech attack detection based on EfficientNet

Zhuoyi Su · 2024

With the rapid development and wide application of speech technology, the use of spoofing speech has become a serious problem. Spoofing speech can be maliciously used to fake information, identity fraud and other activities, which brings serious risks to society. Therefore, effective spoofing speech attack detection methods become essential. In this paper, we propose a spoofing speech attack detection method based on EfficientNet. The feature maps obtained by speech conversion are input into the designed EfficientNet, and a classifier is used to determine the spoofing speech. The experimental results show that the EficientNets-B5 detection model is more than 90% accurate in dealing with various types of spoofing attacks, and the equal error rate is the lowest of 5.82%.

Cited by 0

Restrictive Voting Technique for Faces Spoofing Attack

Mahmoud Omara, Mahmoud Fayez, Heba Khalid et al. · International Journal of Communication Networks and Information Security (IJCNIS) · 2023

Face anti-spoofing has become widely used due to the increasing use of biometric authentication systems that rely on facial recognition. It is a critical issue in biometric authentication systems that aim to prevent unauthorized access. In this paper, we propose a modified version of majority voting that ensembles the votes of six classifiers for multiple video chunks to improve the accuracy of face anti-spoofing. Our approach involves sampling sub-videos of 2 seconds each with a one-second overlap and classifying each sub-video using multiple classifiers. We then ensemble the classifications for each sub-video across all classifiers to decide the complete video classification. We focus on the False Acceptance Rate (FAR) metric to highlight the importance of preventing unauthorized access. We evaluated our method using the Replay Attack dataset and achieved a zero FAR. We also reported the Half Total Error Rate (HTER) and Equal Error Rate (EER) and gained a better result than most state-of-the-art methods. Our experimental results show that our proposed method significantly reduces the FAR, which is crucial for real-world face anti-spoofing applications.

Cited by 0Open access

Spoofing Attack Results Determination in Code Domain Using a Spoofing Process Equation

Beomju Shin, Minhuck Park, Sanghoon Jeon et al. · Sensors · 2019

When a user receiver is tracking an authentic signal, a spoofing signal can be transmitted to the user antenna. The question is under what conditions does the tracking point of the receiver move from the authentic signal to the spoofing signal? In this study, we develop a spoofing process equation (SPE) that can be used to calculate the tracking point of the delay lock loop (DLL) at regular chip intervals for the entire spoofing process. The condition for a successful spoofing signal is analyzed using the SPE. To derive the SPE, parameters, such as the signal strength, sweep velocity, loop filter order, and DLL bandwidth are considered. The success or failure of a spoofing attack is determined for a specific spoofing signal using the SPE. In addition, a correlation between each parameter for a successful spoofing attack could be obtained through the SPE. The simulation results show that the SPE performance is largely consistent with that of general DLL methods, even though the computational load of SPE is very low.

Cited by 6Open access