In the Internet of Audio Things, communication security of the audio control terminal is vulnerable to copy-move threats, and detecting and locating audio copy-move forgery remains challenging nowadays. The forgery detection method based on deep learning achieves higher detection accuracy but fails to localize forged regions. To address this issue, this article proposes an S-Faster R-CNN model for audio copy-move forgery detection and localization. We integrate a novel Similarity Computation Module (SCM) into the Faster R-CNN framework, forming the S-Faster R-CNN model. Obtaining the integration of the SCM, which allows the S-Faster R-CNN to precisely localize forgery regions within the spectrogram. Finally, the image coordinate transformation algorithm is used to map these forged regions to the corresponding locations of the original audio waveform, thus completing the audio copy-move forgery detection and localization. Evaluated on three datasets, our method achieves an average recall of 90%, an average precision of 84%, and an average F1-score of 87%, respectively. Experimental results indicate that the S-Faster R-CNN outperforms state-of-the-art methods in both forgery detection accuracy and especially in localization. Moreover, the proposed method shows good robustness under multiple post-processing.

According to the Buckets effect, the capacity of a bucket depends on the length of the shortest board. This principle also applies to social networking platform resilient (SNPR) image watermarking, which should be comprehensive and free from significant shortcomings. In the frequency domain, the watermarked region is formed using log-polar coordinate mapping (LPM) and has a ring-like structure. However, this structure cannot be stretched or compressed, and it causes a streaking effect at the edges of the watermarked image. These issues have been addressed in the proposed method. Specifically, an adaptive optimization framework is used to adjust the embedding strength and range of the watermark, and multiple synchronization strategies are adopted to correct flip and aspect ratio. Compared with state-of-the-art works, the proposed method significantly improves the imperceptibility of the watermarked image and its robustness to various distortions and lossy transmission on social networking platforms (SNPs).

The need for cancelable biometric techniques has seen a progressive rise due to the rapid deployment of biometric authentication systems. These techniques prevent compromising biometric data by generating and using their corresponding cancelable templates for user authentication. However, the non-invertible distance preserving transformation methods employed in various schemes are often vulnerable to information leakage since matching is performed in the transform domain. This paper proposed a non-invertible distance preserving scheme based on vector permutation and shift-order process. First, the dimension of feature vectors is reduced using kernelized principal component analysis before randomly permuting the extracted vector features. A shift-order process is then applied to the generated features to achieve non-invertibility and combat similarity correlation-based attacks. The generated hash codes are resilient to various security and privacy attacks such as ARM, masquerade, and brute-force preimage. Experimental evaluations conducted on eight fingerprint datasets from FVC2002, FVC2004, and FVC2006 reveal a high matching performance of the proposed method with better recognition accuracy than other existing state-of-the-art. The scheme also fulfills the revocability and unlinkability requirements of cancelable biometrics.

In recent years, online video sharing platforms have been widely available on social networks. To protect copyright and track the origins of these shared videos, some video watermarking methods have been proposed. However, their robustness performance is significantly degraded under geometric deformations, which destroy the synchronization between the watermark embedding and extraction. To this end, we propose a novel robust blind video watermarking scheme by embedding the watermark into low-order recursive Zernike moments. To reduce the time complexity, we give an efficient computation method by exploring the characteristics of video and moments. The moment accuracy is greatly improved due to the introduction of a recursive computation method. Furthermore, we design an optimization strategy to enhance visual quality and reduce distortion drift of watermarked videos by analyzing the radial basis function. The robustness of the proposed scheme is verified by different attacks, including geometric deformations, length-width ratio changes, temporal synchronization attacks, and combined attacks. In practical applications, the proposed scheme effectively resists processing from video sharing platforms and screenshots taken with smartphones and PC monitors. The watermark is extracted without the host video. Experimental results show that our proposed scheme outperforms other state-of-the-art schemes in terms of imperceptibility and robustness.

Adaptive image steganography is the process of embedding secret messages into undetectable regions of a cover image through the design of a distortion function by a steganographer. Since the state-of-the-art steganalyzers are mainly based on image residual analysis, it is reasonable to modify stego image for withstanding steganalysis by reducing or eliminating the image residual distance between cover and stego image. However, simply modifying stego images may lead to message extraction failure and the introduction of additional detectable artifacts. In this paper, we propose a novel secure steganography strategy by constructing immunized stego-image via an artificial immune system, called ISteg, which ensures the accurate extraction of hidden data while enhancing the security against steganalyzers. Inspired by the biological immune system, we use an artificial immune system (AIS) to build ISteg. Specifically, ISteg generates the immunized stego-image by automatically modifying the stego to maximize the affinity of the antibody. The affinity is developed to evaluate antibody quality according to the Euclidean distance between the residual co-occurrence matrix features of the cover image and the modified stego image. In this manner, the so-called immunized stego-image is generated. Extensive experimental results demonstrate that the proposed ISteg strategy can effectively improve the security performance of existing steganography.

Biometric image hashing techniques have been widely studied and seen progressive advancements. However, only a handful of available solutions provide two-factor cancelability while simultaneously satisfying the tradeoff among all criteria of template protection mechanisms. In this paper, we propose a novel scheme for generating a secure and robust hash from a fingerprint image using Fourier-Mellin transform and fractal coding. First, due to its invariance property, Fourier-Mellin transform is incorporated into the domain fingerprint minutiae blocks to provide feature alignment, therein generating a fixed-length minutiae representation for comparison. Then, dimensionality reduction and texture compression are exploited using fractal coding to generate a robust and compact hash for improved security and recognition. The experimental results demonstrate a favorable recognition performance on benchmarked state-of-the-art schemes from FVC2002 and FVC2004 fingerprint databases. The analyses prove our method's robustness and resiliency to security and privacy attacks. Our method also satisfies the revocability and unlinkability criteria of cancelable biometrics.

In this paper, we propose a novel scheme for generating a secure and robust hash using Fourier-mellin transform and fractal coding. First, Fourier-mellin transform is incorporated into the domain minutiae blocks due to its invariance property in order to improve performance under geometric operations, hence generating a fixed-length minutiae representation. Then the property of dimensionality reduction and texture compression is exploited using fractal coding in order to generate a robust and compact hash. To secure the system, encryption is performed on the extracted hash using a secret key. Experimental results demonstrated the robustness of our hashing scheme to a wide range of distortion manipulations. Additionally, performance of the proposed scheme is measured and compared with recent state-of-art techniques, and our scheme generally outperform the others.