skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 5:00 PM ET until 11:00 PM ET on Friday, June 21 due to maintenance. We apologize for the inconvenience.


Title: Whisper: a unilateral defense against VoIP traffic re-identification attacks
Encrypted voice-over-IP (VoIP) communication often uses variable bit rate (VBR) codecs to achieve good audio quality while minimizing bandwidth costs. Prior work has shown that encrypted VBR-based VoIP streams are vulnerable to re-identification attacks in which an attacker can infer attributes (e.g., the language being spoken, the identities of the speakers, and key phrases) about the underlying audio by analyzing the distribution of packet sizes. Existing defenses require the participation of both the sender and receiver to secure their VoIP communications. This paper presents Whisper, the first unilateral defense against re-identification attacks on encrypted VoIP streams. Whisper works by modifying the audio signal before it is encoded by the VBR codec, adding inaudible audio that either falls outside the fixed range of human hearing or is within the human audible range but is nearly imperceptible due to its low amplitude. By carefully inserting such noise, Whisper modifies the audio stream's distribution of packet sizes, significantly decreasing the accuracy of re-identification attacks. Its use is imperceptible by the (human) receiver. Whisper can be instrumented as an audio driver and requires no changes to existing (potentially closed-source) VoIP software. Since it is a unilateral defense, it can be applied at will by a user to enhance the privacy of its voice communications. We demonstrate that Whisper significantly reduces the accuracy of re-identification attacks and incurs only a small degradation in audio quality.  more » « less
Award ID(s):
1718498
NSF-PAR ID:
10178638
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the Annual Computer Security Applications Conference (ACSAC)
Page Range / eLocation ID:
286 to 296
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Data security plays a crucial role in all areas of data transmission, processing, and storage. This paper considers security in eavesdropping attacks over wireless communication links in aeronautical telemetry systems. Data streams in these systems are often encrypted by traditional encryption algorithms such as the Advanced Encryption Standard (AES). Here, we propose a secure coding technique for the integrated Network Enhanced Telemetry (iNET) communications system that can be coupled with modern encryption schemes. We consider a wiretap scenario where there are two telemetry links between a test article (TA) and a legitimate receiver, or ground station (GS). We show how these two links can be used to transmit both encrypted and unencrypted data streams while keeping both streams secure. A single eavesdropper is assumed who can tap into both links through its noisy channel. Since our scheme does not require encryption of the unencrypted data stream, the proposed scheme offers the ability to reduce the size of the required secret key while keeping the transmitted data secure. 
    more » « less
  2. Automatic Speech Recognition (ASR) systems convert speech into text and can be placed into two broad categories: traditional and fully end-to-end. Both types have been shown to be vulnerable to adversarial audio examples that sound benign to the human ear but force the ASR to produce malicious transcriptions. Of these attacks, only the "psychoacoustic" attacks can create examples with relatively imperceptible perturbations, as they leverage the knowledge of the human auditory system. Unfortunately, existing psychoacoustic attacks can only be applied against traditional models, and are obsolete against the newer, fully end-to-end ASRs. In this paper, we propose an equalization-based psychoacoustic attack that can exploit both traditional and fully end-to-end ASRs. We successfully demonstrate our attack against real-world ASRs that include DeepSpeech and Wav2Letter. Moreover, we employ a user study to verify that our method creates low audible distortion. Specifically, 80 of the 100 participants voted in favor of all our attack audio samples as less noisier than the existing state-of-the-art attack. Through this, we demonstrate both types of existing ASR pipelines can be exploited with minimum degradation to attack audio quality. 
    more » « less
  3. null (Ed.)
    Automatic speech recognition and voice identification systems are being deployed in a wide array of applications, from providing control mechanisms to devices lacking traditional interfaces, to the automatic transcription of conversations and authentication of users. Many of these applications have significant security and privacy considerations. We develop attacks that force mistranscription and misidentification in state of the art systems, with minimal impact on human comprehension. Processing pipelines for modern systems are comprised of signal preprocessing and feature extraction steps, whose output is fed to a machine-learned model. Prior work has focused on the models, using white-box knowledge to tailor model-specific attacks. We focus on the pipeline stages before the models, which (unlike the models) are quite similar across systems. As such, our attacks are black-box, transferable, can be tuned to require zero queries to the target, and demonstrably achieve mistranscription and misidentification rates as high as 100% by modifying only a few frames of audio. We perform a study via Amazon Mechanical Turk demonstrating that there is no statistically significant difference between human perception of regular and perturbed audio. Our findings suggest that models may learn aspects of speech that are generally not perceived by human subjects, but that are crucial for model accuracy. 
    more » « less
  4. Collaboration is a 21st Century skill as well as an effective method for learning, so detection of collaboration is important for both assessment and instruction. Speech-based collaboration detection can be quite accurate but collecting the speech of students in classrooms can raise privacy issues. An alternative is to send only whether or not the student is speaking. That is, the speech signal is processed at the microphone by a voice activity detector before being transmitted to the collaboration detector. Because the transmitted signal is binary (1 = speaking, 0 = silence), this method mitigates privacy issues. However, it may harm the accuracy of collaboration detection. To find out how much harm is done, this study compared the relative effectiveness of collaboration detectors based either on the binary signal or high-quality audio. Pairs of students were asked to work together on solving complex math problems. Three qualitative levels of interactivity was distinguished: Interaction, Cooperation and Other. Human coders used richer data (several audio and video streams) to choose the code for each episode. Machine learning was used to induce a detector to assign a code for every episode based on the features. The binary-based collaboration detectors delivered only slightly less accuracy than collaboration detectors based on the high quality audio signal. 
    more » « less
  5. With the fast development of Fifth-/Sixth-Generation (5G/6G) communications and the Internet of Video Things (IoVT), a broad range of mega-scale data applications emerge (e.g., all-weather all-time video). These network-based applications highly depend on reliable, secure, and real-time audio and/or video streams (AVSs), which consequently become a target for attackers. While modern Artificial Intelligence (AI) technology is integrated with many multimedia applications to help enhance its applications, the development of General Adversarial Networks (GANs) also leads to deepfake attacks that enable manipulation of audio or video streams to mimic any targeted person. Deepfake attacks are highly disturbing and can mislead the public, raising further challenges in policy, technology, social, and legal aspects. Instead of engaging in an endless AI arms race “fighting fire with fire”, where new Deep Learning (DL) algorithms keep making fake AVS more realistic, this paper proposes a novel approach that tackles the challenging problem of detecting deepfaked AVS data leveraging Electrical Network Frequency (ENF) signals embedded in the AVS data as a fingerprint. Under low Signal-to-Noise Ratio (SNR) conditions, Short-Time Fourier Transform (STFT) and Multiple Signal Classification (MUSIC) spectrum estimation techniques are investigated to detect the Instantaneous Frequency (IF) of interest. For reliable authentication, we enhanced the ENF signal embedded through an artificial power source in a noisy environment using the spectral combination technique and a Robust Filtering Algorithm (RFA). The proposed signal estimation workflow was deployed on a continuous audio/video input for resilience against frame manipulation attacks. A Singular Spectrum Analysis (SSA) approach was selected to minimize the false positive rate of signal correlations. Extensive experimental analysis for a reliable ENF edge-based estimation in deepfaked multimedia recordings is provided to facilitate the need for distinguishing artificially altered media content. 
    more » « less