Digital watermarking serves as an effective approach for safeguarding speech signal copyrights, achieved by the incorporation of ownership information into the original signal and its subsequent extraction from the watermarked signal. While traditional watermarking methods can embed and extract watermarks successfully when the watermarked signals are not exposed to severe alterations, these methods cannot withstand attacks such as de-synchronization. In this work, we introduce a novel transformer-based framework designed to enhance the imperceptibility and robustness of speech watermarking. This framework incorporates encoders and decoders built on multi-scale transformer blocks to effectively capture local and long-range features from inputs, such as acoustic features extracted by Short-Time Fourier Transformation (STFT). Further, a deep neural networks (DNNs) based generator, notably the Transformer architecture, is employed to adaptively embed imperceptible watermarks. These perturbations serve as a step for simulating noise, thereby bolstering the watermark robustness during the training phase. Experimental results show the superiority of our proposed framework in terms of watermark imperceptibility and robustness against various watermark attacks. When compared to the currently available related techniques, the framework exhibits an eightfold increase in embedding rate. Further, it also presents superior practicality with scalability and reduced inference time of DNN models.

Most of the audio signals in real-world applications are stereo signals. However, the previous desynchronization attacks resilient watermarking methods cannot preserve perceptual quality or achieve robustness when constrained by high embedding rates and stereo host. In this paper, based on two novel features segmental singular values summation (SSVS) and segmental singular values difference (SSVD) that are generated using discrete cosine transform (DCT) and singular value decomposition (SVD), we present a robust watermarking method for stereo signals that not only is robust to desynchronization attacks and common signal processing attacks but also has a larger embedding rate compared with the previous methods. In the proposed method, we first apply DCT and SVD on each segment of the host signal to extract the SSVS feature and the SSVD feature. Then we generate the adaptive embedding parameters and embed watermark bits via optimized embedding strategies based on these features. Due to the use of the adaptive embedding parameters and the optimized embedding strategies, the proposed method significantly increases the embedding rate without compromising the robustness and perceptual quality. Analysis results show our proposed method outperforms the state-of-the-art methods by a large margin, where the perceptual quality improvement is over 14%, and the robustness against desynchronization attacks is improved by more than 49% when the embedding rate is 70 bps.

The collusion attack combines multiple multimedia files into one new file to erase the user identity information. The traditional anti-collusion methods (which aim to trace the traitors) can defend the collusion attack, but they cannot well defend some hybrid collusion attacks (e.g., a collusion attack combined with desynchronization attacks). To address this issue, we propose a frequency spectrum modification process (FSMP) to defend the collusion attack by significantly downgrading the perceptual quality of the colluded file. The severe perceptual quality degradation can demotivate the attackers from launching the collusion attack. Because FSMP is orthogonal to the existing traitor-trace-based methods, it can be combined with the existing methods to provide a double-layer protection against different attacks. In FSMP, after several signal processing procedures (e.g., uneven framing and smoothing), multiple signals (called FSMP signals) can be generated from the host signal. Launching collusion attack using the generated FSMP signals would lead to the energy disturbance and attenuation effect (EDAE) over the colluded signals. Due to the EDAE, FSMP can significantly degrade the perceptual quality of the colluded audio file, thereby thwarting the collusion attack. In addition, FSMP can well defend different hybrid collusion attacks. Theoretical analysis and experimental results confirm the validity of the proposed method.

Abstract Distributed Denial of Service (DDoS) attacks are among the most severe threats in cyberspace. The existing methods are only designed to decide whether certain types of DDoS attacks are ongoing. As a result, they cannot detect other types of attacks, not to mention the even more challenging mixed DDoS attacks. In this paper, we comprehensively analyzed the characteristics of various types of DDoS attacks and innovatively proposed five new features from heterogeneous packets including entropy rate of IP source flow, entropy rate of flow, entropy of packet size, entropy rate of packet size, and number of ICMP destination unreachable packet to detect not only various types of DDoS attacks, but also the mixture of them. The experimental results show that the proposed fives features ranked at the top compared with other common features in terms of effectiveness. Besides, by using these features, our proposed framework outperforms the existing methods when detecting various DDoS attacks and mixed DDoS attacks. The detection accuracy improvements over the existing methods are between 21% and 53%.

Collusion attacks are challenging to tackle in audio fingerprinting. A new direction to resist collusion attacks is to degrade the perceptual quality of the colluded files so that these files cannot be reused. The existing method in this direction has low embedding capacity and limited anti-collusion performance when the number of colluders is odd. In this letter, we present an anti-collusion mechanism that has a higher embedding capacity and can significantly degrade the perceptual quality of the colluded files regardless of the number of colluders. In the proposed mechanism, we first segment the host audio file into frames and perform the discrete cosine transform (DCT) on each frame. Then multiple fingerprint bits are embedded into each frame by reversing the DCT coefficients of the corresponding frequency band. As a result, when a collusion attack occurs, our proposed embedding mechanism can introduce perceptibly annoying differences between frames in the colluded file, which leads to severe perceptual quality degradation. Theoretical analysis and experimental results validate the superiority of the proposed anti-collusion mechanism.

Desynchronization is a very challenging type of attack in audio watermarking. The traditional singular value decomposition (SVD) based audio watermarking methods embed the watermark information by modifying the singular value of individual segment, which have little resistance against desynchronization attacks. In this paper, we propose a novel frequency singular value coefficient (FSVC) feature, which reflects the ratio between the singular values of two consecutive segments and is insensitive to desynchronization attacks, to carry the watermark bits. To our best knowledge, it is the first time that the ratio between singular values is employed for audio watermarking. In the proposed method, the discrete cosine transform (DCT) is performed on two consecutive segments of the host audio signal and SVD is applied to the DCT coefficients of the mid frequency band of each segment to extract the FSVC. Then the watermark bits are embedded by adjusting the values of the FSVC. The watermark embedding procedure is optimized to minimize the perceptual quality degradation and an error buffer is created to enhance the robustness. As a result, the proposed method can achieve a much higher embedding capacity than the existing methods tackling desynchronization attacks. The impact of desynchronization and common signal processing attacks on the proposed watermarking method is mathematically modeled, and the effectiveness of the proposed method against these attacks is theoretically and experimentally validated.

Collusion attacks are considered to be challenging attacks in audio copyright protection. The traditional watermarking algorithms cannot identify the traitors when other attacks, such as desynchronization attacks, are applied with a collusion attack. Instead of tracing the traitors, in this paper we aim to tackle collusion attacks by removing the commercial value from the colluded copy, which will demotivate the attackers from launching collusion attacks. Since the commercial value of an audio signal is directly reflected by its perceptual quality, we propose a novel non-linear-echo generation (NLEG) based algorithm to significantly degrade the perceptual quality of the colluded copy by embedding a time delay sequence into the host signal. The proposed NLEG is also designed to be resilient to common signal processing attacks and desynchronization attacks. Furthermore, the proposed NLEG can be combined with other digital watermarking techniques to enhance its performance on protecting the copyright information. Experimental results show the validity of the proposed NLEG.

Cropping and random bending are two common attacks in image watermarking. In this paper we propose a novel image-watermarking method to deal with these attacks, as well as other common attacks. In the embedding process, we first preprocess the host image by a Gaussian low-pass filter. Then, a secret key is used to randomly select a number of gray levels and the histogram of the filtered image with respect to these selected gray levels is constructed. After that, a histogram-shape-related index is introduced to choose the pixel groups with the highest number of pixels and a safe band is built between the chosen and nonchosen pixel groups. A watermark-embedding scheme is proposed to insert watermarks into the chosen pixel groups. The usage of the histogram-shape-related index and safe band results in good robustness. Moreover, a novel high-frequency component modification mechanism is also utilized in the embedding scheme to further improve robustness. At the decoding end, based on the available secret key, the watermarked pixel groups are identified and watermarks are extracted from them. The effectiveness of the proposed image-watermarking method is demonstrated by simulation examples.

Reversible watermarking has merged over the past few years as a promising solution for copyright protection, especially for applications like remote sensing, medical imaging and military applications which require lossless recovery of the host media. In this paper, we aim to extend the additive interpolation error expansion technique in [16]. We will consider the human visual system (HVS) to improve the embedding rate while maintaining the image visual quality. To this end, the just noticeable difference (JND) is used to embed more watermark bits. The experimental results show that the proposed algorithm can improve the embedding rate while preserving the image visual quality.

This paper presents a novel adaptive safe-band for quantization based audio watermarking methods, aiming to improve robustness. Considerable number of audio watermarking methods have been developed using quantization based techniques. These techniques are generally vulnerable to signal processing attacks. For these conventional quantization based techniques, robustness can be marginally improved by choosing larger step sizes at the cost of significant perceptual quality degradation. We first introduce fixed size safe-band between two quantization steps to improve robustness. This safe-band will act as a buffer to withstand certain types of attacks. Then we further improve the robustness by adaptively changing the size of the safe-band based on the audio signal feature used for watermarking. Compared with conventional quantization based method and the fixed size safe-band based method, the proposed adaptive safe-band based quantization method is more robust to attacks. The effectiveness of the proposed technique is demonstrated by simulation results.

Developing a watermarking method that is robust to cropping attack and random bending attacks (RBAs) is a challenging task in image watermarking. In this paper, we propose a histogram-based image watermarking method to tackle with both cropping attack and RBAs. In this method first the gray levels are divided into groups. Secondly the groups for watermark embedding are selected according to the number of pixels in them, which makes this method fully based on the histogram shape of the original image and adaptive to different images. Then the watermark bits are embedded by modifying the histogram of the selected groups. Since histogram shape is insensitive to cropping and independent from pixel positions, the proposed method is robust to cropping attack and RBAs. Besides, it also has high robustness against other common attacks. Experimental results demonstrate the effectiveness of the proposed method.