Watermarking is a solution for copyright protection and forensics tracking, but recapturing and de-synchronization attacks may be used to effectively remove audio watermarks. Although much effort has been made in recent years, the robustness of audio watermarking against recapturing and de-synchronization attacks is still a challenging issue. Specifically, we first construct the frequency-domain coefficients logarithmic mean (FDLM) feature of digital audio. By theoretical analysis, we conclude that the residual of the two groups' FDLM feature is robust against recapturing attack. We then propose a robust audio watermarking method based on this feature using the patchwork framework. Compared with the method having the best robustness performance against recapturing attack, the BER value of our method is decreased by 7%. Besides that, the proposed method outperforms the state-of-the-art patchwork-based watermarking methods notably, under recapturing and post-processed with signal processing operations and de-synchronization attacks.

Digital watermarking is a widely adopted authentication technique and one of its primary concerns in practical usage is robustness. However, existing audio watermarking methods face challenges in countering desynchronization attacks and replay attacks, which can easily lead to watermark extraction failure. In this paper, we introduce a learning-based scheme, named DRAW (Dual-decoder-based Robust Audio Watermarking), to overcome the robustness issue. Specifically, a watermark encoder embeds payloads together with synch codes into audio frames with high imperceptibility. For reliable watermark extraction, two separate decoders are designed, one for Fixed Length Synchronization Decoding (FLSD) and the other for Variable Length Payload Decoding (VLPD). The dual decoders are trained with the encoder with different weights in the loss function by considering their different roles for watermark extraction. To better resist attacks, a distortion layer is incorporated in-between the encoder and the decoders to simulate distortion and facilitate end-to-end learning. For the more challenging replay attacks, a pre-trained Replay Attack Simulation Network (RASN) is applied to simplify the simulation of re-recording with background noise and reverberation. Extensive experimental results show that the proposed method can be applied to variable-length audio clips with better auditory quality, and it outperforms state-of-the-art methods in robustness against various kinds of attacks.

There are a large amount of audio signals stored in third-party storage centers. To improve the privacy and security of audio signals, a digital audio encryption and forensics watermarking scheme is proposed. We defined the signal energy ratio feature of audio signals and designed the embedding method by quantifying the feature, aiming to improve the security of watermarking system. We firstly encrypted the original audio by using scrambling and multiplication, to get the encrypted data. Secondly, we divided the encrypted data into frames and compressed each frame by sampling to get the compressed data. After that, we embedded the compressed data and frame number into the encrypted data to get the watermarked signal being uploaded to third-party storage centers. Authorized users download encrypted data and verify the authenticity of the data. For the intact data, they decrypt it directly to get the audio signal. If the downloaded data has been attacked, they locate the attacked frame and extract the compressed data to reconstruct the attacked signal approximately. Furthermore, we decrypted the reconstructed signal to obtain the expression meaning of the original audio. The experimental results demonstrate the effectiveness of the proposed scheme.

Nowadays, many users make money by publishing content on social media platforms. In order to attract users’ attention, they often take measures to promote themselves. The security vulnerabilities in social media platforms may provide convenience for their user promotion work. We call this type of vulnerability the user promotion security vulnerability (UPSV). UPSV may cause unfair competition and endanger the interests of legitimate users and the social media platforms. Therefore it has great research significance to find and fix this vulnerability. In this paper, we propose a UPSV which widely exists in the function of sending messages to strangers of in-app chatting of many social media platforms. We first analyzed this vulnerability in some apps, and then YY app (China’s largest live streaming platform) was chosen as the research object to verify the actual effect of the vulnerability on illegitimate user promotion. We took the method of promoting a target YY streamer through sending promotional links to viewers, and to improve promotion effect, we used the method of user preference learning to select viewers for promotion. The experimental results show that among the promoted viewers, more than 44% entered the target streamers’ channels to watch live streaming, more than 21% followed the target steamers, and more than 13% gave gifts to the target steamers. It fully proves that this UPSV is real, exploitable and harmful, and we also proposed some fix suggestions to help the platforms to fix it.