A semi fragile watermarking algorithm based on compressed sensing applied for audio tampering detection and recovery
A semi fragile watermarking algorithm based on compressed sensing applied for audio tampering detection and recovery
复制标题
基于压缩感知的半脆弱水印算法应用于音频篡改检测与恢复
DOI：
10.1007/s11042-022-12719-0
复制
发表时间：
2022-03
影响因子：
3.6
通讯作者：
Jianguo Wei
中科院分区：
文献类型：
--
作者：
Yangxia Hu;Wenhuan Lu;Maode Ma;Qilong Sun;Jianguo Wei
音频篡改检测与恢复技术的研究在音频完整性、真实性认证等领域具有重要的意义。通常，我们使用脆弱/半脆弱水印技术来检测和恢复被篡改的音频。在这项研究中，提出了一种新的方案，水印嵌入，篡改检测和恢复。在新方案中，我们使用压缩感知技术获得原始音频信号的压缩版本，并对每个音频帧进行离散小波变换（DWT）。在嵌入过程中，提出了一种新的自适应算法。水印是原始成帧音频信号和篡改位置数据的量化参考值，经两级小波变换后分别嵌入到高频系数能量较低和低频系数能量较高的区域。在检测过程中，通过将产生的随机数和提取的水印进行异或运算后的值与提取的位置数据进行比较来定位篡改区域。对于语音，我们设置了一个阈值来判断它是否被篡改。最后，在未被破坏的区域提取水印，解压缩后得到恢复的信号。实验和分析表明，嵌入后的信号平均信噪比比未嵌入前提高了5dB以上，能够准确地检测出断帧和断组。当信号被破坏20%时，恢复后98%的语料可懂，即使被破坏50%，恢复后80%的语料也可懂。与其他恢复算法相比，该算法恢复的音频信号具有更高的信噪比和更好的鲁棒性。当篡改率为50%时，平均检测率超过93%，表明该方法是可行的。
Research on audio tampering detection and recovery plays an important role in the field of audio integrity, and authenticity certification. Generally, we use technology of fragile/semi fragile watermarking to detect and recover tampered audio. In this study, a new scheme for watermark embedding, tampering detection, and recovery is proposed. In the new scheme, we get the compressed version of original audio signal using compressed sensing technology and apply discrete wavelet transform (DWT) to each audio frame. In process of embedding, a new self-adaptive algorithm is proposed. Watermark is the quantized reference value of original framed audio signal and tampering location data, and is embedded in the region with low energy of high frequency coefficients and high energy of low frequency coefficients respectively after 2-level DWT. In process of detection, we locate tampered areas by comparing the value of generated random number and extracted watermark after XOR operation with the extracted location data. As for speech, we set a threshold to judge whether it is tampered or not. At last, we extract watermark in areas which are not damaged and get the recovered signal after decompression. Experiments and analysis show that signal after embedding has at least 5 dB higher average signal-to-noise ratio than others, and broken frames and groups can be detected exactly. When signal is destroyed by 20%, 98% of the corpus is intelligible after recovery, and even destroyed by 50%, 80% of the corpus recovered is also intelligible. Compared with other recovery algorithms, audio signal recovered by our proposal has a higher signal-to-noise ratio and a better robustness to some signal processing. When tampering rate is 50%, the average detection rate is over 93%, which indicates that our method is workable.
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影响因子：
5.4
作者：
S. Larbi;Mériem Jaïdane
通讯作者：
S. Larbi;Mériem Jaïdane
影响因子：
10.6
作者：
Pawel Korus;A. Dziech
通讯作者：
Pawel Korus;A. Dziech
DOI：
10.1109/icct.2017.8359932
发表时间：
2017-10
期刊：
2017 IEEE 17th International Conference on Communication Technology (ICCT)
影响因子：
--
作者：
Nan Wang;Zhi Li;Xinyu Cheng;Yi Chen
通讯作者：
Nan Wang;Zhi Li;Xinyu Cheng;Yi Chen
影响因子：
--
作者：
Cheonshik Kim;Dongkyoo Shin;Ching-Nung Yang
通讯作者：
Cheonshik Kim;Dongkyoo Shin;Ching-Nung Yang
影响因子：
3.9
作者：
Hwai-Tsu Hu;Tung-Tsun Lee
通讯作者：
Hwai-Tsu Hu;Tung-Tsun Lee