Image encryption is an efficient and vital way to protect classified and secret images. With the advancement of the processing power of the computer, AES, DES or chaotic series type just alike image encryption schemes are not as secure as before. So in this paper, we present a new hybrid image encryption method for protecting secret and imperative images by employing Logistic Sine System (LSS) together with two-dimensional Cellular Automata and FSM based DNA rule generator. Secure hash (SHA-256) algorithm is used to generate a secret key and to compute initial values for the LSS. In our proposed method there are three stages and each stage has its own rule. After the scrambling process, the first stage is the Feistel structure based bit inversion (FSBI) to change the pixels value. The second stage is 2D-CA with Moore neighborhood structure based local rules. The third is DNA conversion based on Finite State Machine (FSM-DNA) rule generator. The proposed encryption scheme is robust against well-known attacks such as statistical attacks, brute force attacks, differential attacks, and pixel correlation attacks and also possess strong key sensitivity. Results show that our three-layer hybrid image encryption technique is robust against many well-known attacks and can be applied directly to all type of classified gray-scale images to make them more secure from such cryptography attacks.

Several secure image encryption systems have been researched and formed by chaotic mechanisms in current decades. This work recommends an innovative quantum color image encryption method focused on the Lucas series-based substitution box to enhance the competence of encryption. The suggested encryption technique has more excellent key space and significant confidentiality. The chaotic system, along with the substitution box, exhibits additional complicated dynamical behavior, sufficient arbitrariness, and uncertainty than all others focused on just chaotic models. Theoretical and simulation assessments show that the offered image encryption performs admirably, its traditional equivalents in terms by efficiency in terms of statistical analysis.

Bit-level and pixel-level methods are two classifications for image encryption, which describe the smallest processing elements manipulated in diffusion and permutation respectively. Most pixel-level permutation methods merely alter the positions of pixels, resulting in similar histograms for the original and permuted images. Bit-level permutation methods, however, have the ability to change the histogram of the image, but are usually not preferred due to their time-consuming nature, which is owed to bit-level computation, unlike that of other permutation techniques. In this paper, we introduce a new image encryption algorithm which uses binary bit-plane scrambling and an SPD diffusion technique for the bit-planes of a plain image, based on a card game trick. Integer values of the hexadecimal key SHA-512 are also used, along with the adaptive block-based modular addition of pixels to encrypt the images. To prove the first-rate encryption performance of our proposed algorithm, security analyses are provided in this paper. Simulations and other results confirmed the robustness of the proposed image encryption algorithm against many well-known attacks; in particular, brute-force attacks, known/chosen plain text attacks, occlusion attacks, differential attacks, and gray value difference attacks, among others.

This paper proposes a robust image encryption algorithm that utilizes a Novel three-dimensional (3D) Chaotic map and an Enhanced Logistic Sine System (ELSS). We leverage the unpredictability of 3D chaotic dynamics alongside the complexity of ELSS and DNA Sequence to forge a formidable image encryption scheme. Firstly, the image pixels are converted from decimal to hexadecimal notation and sorted in a 1D pixel array carrying a unique sequence of three channels of the RGB image. Secondly, the secret key is appended to XOR, the values with that 1-D pixels array. Thirdly, values are sorted by performing the binary right shift operation and encoded into DNA. Fourthly, a novel chaotic map is used to perform scrambling operations. Lastly, a novel enormous keyspace ELSS is used to perform efficient Border and Cross-Border (B &CB) pixel exchange, further enhancing the encryption quality of the proposed algorithm. Comprehensive security analysis proved that the proposed algorithm exhibits remarkable resilience against powerful known and chosen plaintext attacks and other prevalent cryptanalysis attacks, including differential attacks and exhaustive key search attacks. Henceforth, the proposed algorithm’s superior security and low computational cost make it an ideal choice for real-time secure image communication across various platforms, including satellite, multimedia, and military communications.

The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.