Demo Data from VLDB 2026 / reviewers in the wild / expert
Yousof Erfani
dblp:95/2944
· DBLP profile ↗
3ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0001-8386-3765ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Security and privacy · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1
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 |
Digital forensics and information hiding · 100% | |
| Computer graphics and multimedia
1 paper |
Audio and music processing · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Digital forensics and information hiding › watermarking
audio watermarking |
0.3 | 1 | 2017 | Audio Watermarking Using Spikegram and a Two-Dictionary Approach · IEEE Trans. Inf. Forensics Secur. 2017 |
Audio and music processing
audio representation |
0.1 | 1 | 2017 | Audio Watermarking Using Spikegram and a Two-Dictionary Approach · IEEE Trans. Inf. Forensics Secur. 2017 |
Methods — techniques the papers use, named apart from their topics
two-dictionary approach · 0.6phase embedding · 0.6
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | A novel audio watermarking scheme using ensemble-based watermark detector and discrete wavelet transform
Seyed Mostafa Pourhashemi, Mohammad Mosleh, Yousof Erfani |
Neural Comput. Appl. | 3 |
| 2019 | Audio watermarking based on synergy between Lucas regular sequence and Fast Fourier Transform
Seyed Mostafa Pourhashemi, Mohammad Mosleh, Yousof Erfani |
Multim. Tools Appl. | 3 |
| 2017 | Audio Watermarking Using Spikegram and a Two-Dictionary ApproachabstractThis paper introduces a new audio watermarking technique based on a perceptual kernel representation of audio signals (spikegram). Spikegram is a recent method to represent audio signals. It is combined with a dictionary of gammatones to construct a robust representation of sounds. In traditional phase embedding methods, the phase of coefficients of a given signal in a specific domain (such as Fourier domain) is modified. In the encoder of the proposed method (two-dictionary approach), the signs and the phases of gammatones in the spikegram are chosen adaptively to maximize the strength of the decoder. Moreover, the watermark is embedded only into kernels with high amplitudes, where all masked gammatones have been already removed. The efficiency of the proposed spikegram watermarking is shown via several experimental results. First, robustness of the proposed method is shown against 32 kb/s MP3 with an embedding rate of 56.5 b/s. Second, we showed that the proposed method is robust against unified speech and audio codec (24-kb/s USAC, linear predictive, and Fourier domain modes) with an average payload of 5-15 b/s. Third, it is robust against simulated small real room attacks with a payload of roughly 1 b/s. Last, it is shown that the proposed method is robust against a variety of signal processing transforms while preserving quality. Yousof Erfani, Ramin Pichevar, Jean Rouat |
IEEE Trans. Inf. Forensics Secur. | 1 |