A new image encryption method based on rotation transformation and chaos theory was proposed, which used chaos function to generate random phase mask (chaotic random phase mask). In the proposed technique, the image was encrypted using gyrator transform and two chaotic random phase masks. In the proposed method, the image was encrypted by using rotation transform and two chaotic random phase masks, and the chaotic random phase mask was generated by using three chaotic functions: Logistic map, Tent map and Kaplan⁃Yorke map. The computer simulations were presented to verify the validity of the proposed technique. By calculating the mean square deviation, the robustness of the blind decryption algorithm based on the seed value of rotation angle and chaotic random phase was evaluated, and the optical implementation scheme of encryption and decryption technology was given.

In this paper, a new watermarking scheme based on Chaos for image authentication and tamper detection is proposed. This scheme can detect any modification to the image and determine the specific location of the modification. In order to improve the security of the proposed scheme, two chaotic maps are used. Because the chaotic map is sensitive to the initial value, the corresponding position relationship between the pixels in the watermarking image and the watermarking is disturbed, which makes the watermarking scheme resist forgery attack. In this scheme, the initial value of the chaotic map is used as the secret key, and the effectiveness of the proposed scheme is checked through a series of attacks. The experimental results show that the scheme is not only safe and reliable, but also can achieve high tamper detection and location accuracy under different attacks.

Voice forgery is detected by digital audio watermarking and pattern recovery techniques. The three methods used by digital watermarking mode for speech signal forgery detection are replacement, insertion and deletion. If some changes are made to the speech signal, the watermark mode will also change, and the mode recovery can be used to measure or detect whether the speech signal has been modified or forged. The proposed method uses the cyclic pattern embedding to overcome synchronizing problems of previous detection techniques. In addition, pattern recovery enhances the robustness to compression. This method has been tested and verified using six recording devices, which was used for collecting verbal data. The speech signals were sampled at the rate of 8 kHz and digitized at 16 bits resolution. Randomly chosen regions were substituted, removed, and compressed in MP3 at the rate of 16 kbps as well as in CELP at the rate of 11.5 kbps. The experiment shows the perfect detection for three kinds of forgeries and it proved the validity of the proposed method.