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In this work, a color image encryption and decryption algorithm for digital images is presented. It is based on the modular discrete derivative (MDD), a novel technique to encrypt images and efficiently hide visual information. In addition, Langton’s ant, which is a two-dimensional universal Turing machine with a high key space, is used. Moreover, a deterministic noise technique that adds security to the MDD is utilized. The proposed hybrid scheme exploits the advantages of MDD and Langton’s ant, generating a very secure and reliable encryption algorithm. In this proposal, if the key is known, the original image is recovered without loss. The method has demonstrated high performance through various tests, including statistical analysis (histograms and correlation distributions), entropy, texture analysis, encryption quality, key space assessment, key sensitivity analysis, and robustness to differential attack. The proposed method highlights obtaining chi-square values between 233.951 and 281.687, entropy values between 7.9999225223 and 7.9999355791, PSNR values (in the original and encrypted images) between 8.134 and 9.957, the number of pixel change rate (NPCR) values between 99.60851796% and 99.61054611%, unified average changing intensity (UACI) values between 33.44672377% and 33.47430379%, and a vast range of possible keys >5.8459×1072. On the other hand, an analysis of the sensitivity of the key shows that slight changes to the key do not generate any additional information to decrypt the image. In addition, the proposed method shows a competitive performance against recent works found in the literature.

This paper presents a modular system for both abnormal event detection and categorization in videos. Complementary normalcy models are built both globally at the image level and locally within pixels blocks. Three features are analyzed: (1) spatio-temporal evolution of binary motion where foreground pixels are detected using an enhanced background subtraction method that keeps track of temporarily static pixels; (2) optical flow, using a robust pyramidal KLT technique; and (3) motion temporal derivatives. At the local level, a normalcy MOG model is built for each block and for each flow feature and is made more compact using PCA. Then, the activity is analyzed qualitatively using a set of compact hybrid histograms embedding both optical flow orientation (or temporal gradient orientation) and foreground statistics. A compact binary signature of maximal size 13 bits is extracted from these different features for event characterization. The performance of the system is illustrated on different datasets of videos recorded on static cameras. The experiments show that the anomalies are well detected even if the method is not dedicated to one of the addressed scenarios.