Demo Data from VLDB 2026 / reviewers in the wild / expert
Bin Gong 0001
dblp:71/5780-1
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0006-9177-2947ORCID · verified
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Network and information security
1 paper |
Cyber-physical and IoT security · 54% Network security · 23% Hardware security and side channels · 23% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Network security › intrusion detection and prevention › intrusion detection
attack detection |
0.8 | 1 | 2024 | Indelible "Footprints" of Inaudible Command Injection · IEEE Trans. Inf. Forensics Secur. 2024 |
Hardware security and side channels
hardware fingerprinting |
0.8 | 1 | 2024 | Indelible "Footprints" of Inaudible Command Injection · IEEE Trans. Inf. Forensics Secur. 2024 |
Cyber-physical and IoT security › voice assistant security
inaudible voice command attack |
0.8 | 1 | 2024 | Indelible "Footprints" of Inaudible Command Injection · IEEE Trans. Inf. Forensics Secur. 2024 |
Cyber-physical and IoT security
voice assistant security |
0.8 | 1 | 2024 | Indelible "Footprints" of Inaudible Command Injection · IEEE Trans. Inf. Forensics Secur. 2024 |
Cyber-physical and IoT security › sensor security
ultrasound attack |
0.2 | 1 | 2024 | Indelible "Footprints" of Inaudible Command Injection · IEEE Trans. Inf. Forensics Secur. 2024 |
Methods — techniques the papers use, named apart from their topics
spectrogram analysis · 0.8signal processing · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Indelible "Footprints" of Inaudible Command InjectionabstractInaudible command injection transmits inaudible ultrasounds to inject adversarial speech commands into a voice assistant, therefore manipulating voice control systems (e.g., a garage door or a security camera) for illegitimate purposes. Although the attack is inaudible, we find it does leave visible “footprints”. Such attack “footprints” are the side product due to the interaction between the attack signal (i.e., input) and the acoustic components (i.e., transfer function), so they reflect the hardware characteristics of the sound capture system, including the microphone diaphragm, the low-pass filter, and the analog-to-digital converter. Moreover, unlike the non-linearity distortion that is erasable with signal-shaping techniques, the “footprints” are indelible because they are unrelated to the content of injected commands. We discover two types of indelible “footprints” embedded in the recording spectrogram, namely abnormal interfering noise and abnormal demodulation. A software-based detection method and a portable detector, DolphinTag, are further designed to identify these “footprints”. The software-based method achieves a detection accuracy of 99.8% on the phone models exhibiting abnormal interfering noise, and our DolphinTag achieves 100% detection accuracy which detects the ultrasound attack by actively facilitating the abnormal demodulation. Zhongjie Ba, Bin Gong 0001, Yuwei Wang 0009, Peng Cheng 0007, Feng Lin 0004, Li Lu 0008, Kui Ren 0001 |
IEEE Trans. Inf. Forensics Secur. | 2 |