Biblio

Automatic Speech Verification (ASV) systems are highly vulnerable to spoofing attacks, and replay attack poses the greatest threat among various spoofing attacks. In this paper, we propose a novel multi-channel feature extraction method with attention-based adaptive filters (AAF). Original phase information, discarded by conventional feature extraction techniques after Fast Fourier Transform (FFT), is promising in distinguishing genuine from replay spoofed speech. Accordingly, phase and magnitude information are respectively extracted as phase channel and magnitude channel complementary features in our system. First, we make discriminative ability analysis on full frequency bands with F-ratio methods. Then attention-based adaptive filters are implemented to maximize capturing of high discriminative information on frequency bands, and the results on ASVspoof 2017 challenge indicate that our proposed approach achieved relative error reduction rates of 78.7% and 59.8% on development and evaluation dataset than the baseline method.

Even if information hiding can be used for licit purposes, it is increasingly exploited by malware to exfiltrate data or to coordinate attacks in a stealthy manner. Therefore, investigating new methods for creating covert channels is fundamental to completely assess the security of the Internet. Since the popularity of the carrier plays a major role, this paper proposes to hide data within VoIP traffic. Specifically, we exploit Voice Activity Detection (VAD), which suspends the transmission during speech pauses to reduce bandwidth requirements. To create the covert channel, our method transforms a VAD-activated VoIP stream into a non-VAD one. Then, hidden information is injected into fake RTP packets generated during silence intervals. Results indicate that steganographically modified VAD-activated VoIP streams offer a good trade-off between stealthiness and steganographic bandwidth.

In this proposed method, the traditional elevators are upgraded in such a way that any alarming situation in the elevator can be detected and then sent to a main center where further action can be taken accordingly. Different emergency situation can be handled by implementing the system. Smart elevator system works by installing different modules inside the elevator such as speed sensors which will detect speed variations occurring above or below a certain threshold of elevator speed. The smart elevator system installed within the elevator sends a message to the emergency response center and sends an automated call as well. The smart system also includes an emotion detection algorithm which will detect emotions of the individual based on their expression in the elevator. The smart system also has a whisper detection system as well to know if someone stuck inside the elevator is alive during any hazardous situation. A broadcast signal is used as a check in the elevator system to evaluate if every part of the system is in stable state. Proposed system can completely replace the current elevator systems and become part of smart homes.

In this paper, an edit detection method for forensic audio analysis is proposed. It develops and improves a previous method through changes in the signal processing chain and a novel detection criterion. As with the original method, electrical network frequency (ENF) analysis is central to the novel edit detector, for it allows monitoring anomalous variations of the ENF related to audio edit events. Working in unsupervised manner, the edit detector compares the extent of ENF variations, centered at its nominal frequency, with a variable threshold that defines the upper limit for normal variations observed in unedited signals. The ENF variations caused by edits in the signal are likely to exceed the threshold providing a mechanism for their detection. The proposed method is evaluated in both qualitative and quantitative terms via two distinct annotated databases. Results are reported for originally noisy database signals as well as versions of them further degraded under controlled conditions. A comparative performance evaluation, in terms of equal error rate (EER) detection, reveals that, for one of the tested databases, an improvement from 7% to 4% EER is achieved, respectively, from the original to the new edit detection method. When the signals are amplitude clipped or corrupted by broadband background noise, the performance figures of the novel method follow the same profile of those of the original method.