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This paper models the messages embedded by spatial least significant bit (LSB) matching as independent noises to the cover image, and reveals that the histogram of the differences between pixel gray values is smoothed by the stego bits despite a large distance between the pixels. Using the characteristic function of difference histogram ( DHCF ), we prove that the center of mass of DHCF ( DHCF COM ) decreases after messages are embedded. Accordingly, the DHCF COMs are calculated as distinguishing features from the pixel pairs with different distances. The features are calibrated with an image generated by average operation, and then used to train a support vector machine (SVM) classifier. The experimental results prove that the features extracted from the differences between nonadjacent pixels can help to tackle LSB matching as well.

With the proliferation of image-capturing and display-enabled IoT devices, ensuring the authenticity and integrity of visual data has become increasingly critical, especially in light of emerging cybersecurity threats and powerful generative AI tools. One of the major challenges in such sensor-based systems is the ability to protect privacy while maintaining data usability. Reversible data hiding has attracted growing attention due to its reversibility and ease of implementation, making it a viable solution for secure image communication in IoT environments. In this paper, we propose reversible data hiding techniques tailored to the content characteristics of images. Our approach leverages subsampling and quadtree partitioning, combined with multi-stage prediction schemes, to generate a predicted image aligned with the original. Secret information is embedded by analyzing the difference histogram between the original and predicted images, and enhanced through multi-round rotation techniques and a multi-level embedding strategy to boost capacity. By employing both subsampling and quadtree decomposition, the embedding strategy dynamically adapts to the inherent characteristics of the input image. Furthermore, we investigate the trade-off between embedding capacity and marked image quality. Experimental results demonstrate improved embedding performance, high visual fidelity, and low implementation complexity, highlighting the method’s suitability for resource-constrained IoT applications.

Surveillance systems regularly create massive video data in the modern technological era, making their analysis challenging for security specialists. Finding anomalous activities manually in these enormous video recordings is a tedious task, as they infrequently occur in the real world. We proposed a minimal complex deep learning-based model named EADN for anomaly detection that can operate in a surveillance system. At the model’s input, the video is segmented into salient shots using a shot boundary detection algorithm. Next, the selected sequence of frames is given to a Convolutional Neural Network (CNN) that consists of time-distributed 2D layers for extracting salient spatiotemporal features. The extracted features are enriched with valuable information that is very helpful in capturing abnormal events. Lastly, Long Short-Term Memory (LSTM) cells are employed to learn spatiotemporal features from a sequence of frames per sample of each abnormal event for anomaly detection. Comprehensive experiments are performed on benchmark datasets. Additionally, the quantitative results are compared with state-of-the-art methods, and a substantial improvement is achieved, showing our model’s effectiveness.

This paper focuses on retrieving plant leaf images based on different features that can be useful in the plant industry. Various images and their features can be used to identify the type of leaf and its disease. For this purpose, a well-organized computer-assisted plant image retrieval approach is required that can use a hybrid combination of the color and shape attributes of leaf images for plant disease identification and botanical gardening in the agriculture sector. In this research work, an innovative framework is proposed for the retrieval of leaf images that uses a hybrid combination of color and shape features to improve retrieval accuracy. For the color features, the Color Difference Histograms (CDH) descriptor is used while shape features are determined using the Saliency Structure Histogram (SSH) descriptor. To extract the various properties of leaves, Hue and Saturation Value (HSV) color space features and First Order Statistical Features (FOSF) features are computed in CDH and SSH descriptors, respectively. After that, the HSV and FOSF features of leaf images are concatenated. The concatenated features of database images are compared with the query image in terms of the Euclidean distance and a threshold value of Euclidean distance is taken for retrieval of images. The best results are obtained at the threshold value of 80% of the maximum Euclidean distance. The system’s effectiveness is also evaluated with different performance metrics like precision, recall, and F-measure, and their values come out to be respectively 1.00, 0.96, and 0.97, which is better than individual feature descriptors.

In spatial domain image steganography, Least Significant Bits (LSB) of cover image pixels are used to embed a secret message due to minimal distortion and higher payload capacity. In this paper, we have introduced an exclusive-OR (XOR) based encoding of encrypted secret message bits using varying higher-order pixel intensity bits. Encoding and LSB embedding is done block-wise by dividing the cover image into a number of blocks. The secret message is first encrypted using symmetric key cryptography and then encoded those encrypted bits by XORing them with randomly selected higher-order pixel bis of the cover image to obscure the secret bits further. Next, an inversion technique is applied to the encoded bits block-wise to keep the LSB bit changes to a minimum. The stego-key consists of the symmetric encryption key and the encode-key containing parameter settings such as the number_of_blocks, starting_block, start_pixel_offset, block_selection_rule, etc. This stego-key is shared prior to the actual communication using public-key cryptography to ensure the key’s authenticity and integrity. The extraction process does not require the cover image; the stego-image and the stego-key are sufficient. Experimental results show the visual imperceptibility along with improved image quality metrics such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Normalized Cross-Correlation (NCC), and Structural Similarity (SSIM) index in comparison to other well-known techniques. The average PSNR value remains above 51dB, even with 90% of the capacity utilized. The proposed scheme successfully eludes many standard steganalysis attacks such as histogram-based analysis (PDH), chi-square based embed probability test, Regular and Singular groups (RS) analysis, sample pair test, etc. on the tested stego-images.

Humanity's survival, development, and existence are deeply intertwined with agriculture, the source of most of our food. Plant disease detection helps in securing food, but manual plant disease detection is error-prone and labor-intensive. Convolutional Neural Networks (CNNs) are highly effective for automated plant disease classification, but their difficulty in recognizing differently oriented images means they need large datasets with many variations to work best. Capsule Networks (CapsNets) were developed to overcome the shortcomings of CNNs and can function effectively with smaller datasets. However, CapsNets process every part of an input image, so their performance can suffer when dealing with complex visuals. To tackle this challenge, DLCA-CapsNet was introduced. DLCA-CapsNet integrates a Color Difference Histogram (CDH) layer for key feature extraction, atrous convolution layers to enlarge receptive fields while maintaining spatial details, along with max-pooling, standard convolutional layers, and a dropout layer. The proposed DLCA-CapsNet method was evaluated on datasets including apple, banana, grape, maize, mango, pepper, potato, rice, tomato, as well as CIFAR-10 and Fashion-MNIST. The model demonstrated strong performance with high test accuracies in plant disease detection and on CIFAR-10 and Fashion-MNIST. It improved test accuracies by 6.78%, 14.82%, 6.14%, 5.07%, 21.12%, 40.32%, 4.64%, 0.76%, 10.23%, 13.73%, and 2.03%, while also reducing the number of parameters in millions by 6.16M, 6.16M, 6.16M, 6.16M, 7.14M, 5.68M, 5.92M, 7.62M, 7.62M, and 6.54M respectively when compared with the original CapsNet. On sensitivity, F1-Score, precision, specificity, Receiver Operating Characteristics, Precision-Recall values, accuracy, disk size, and parameters generated, etc., the DLCA-CapsNet achieved better performance compared to the original CapsNet and other advanced CapsNets reported in the literature. The findings suggest that this efficient and computationally less demanding method can significantly enhance plant disease classification and contribute incrementally to efforts aligned with the SDG 2 goal by offering a lightweight, scalable solution that can be adapted for field use in resource-constrained settings.

As electronic warfare becomes the core of modern warfare, radio frequency (RF) stealth radar is becoming the focus of modern electronic warfare. The anti-sorting strategy of RF stealth radar is a new effective method of the anti-enemy electronic reconnaissance system. Hence, research on the failure mechanism of sorting algorithms has become the cornerstone of anti-sorting technology. In this paper, the mechanism of algorithm failure is studied because the SDIF algorithm is widely used in engineering practice throughout the whole workflow of the sequential difference histogram (SDIF) algorithm, from the estimation of pulse repetition interval (PRI) destroyed by the algorithm and algorithm analysis to the staggered signal. Firstly, the working steps of the SDIF histogram sorting algorithm are considered. Key steps of signal sorting by the algorithm are analyzed, and the failure principle of the sorting algorithm is proposed. It is pointed out that if the PRI signal center value of the two groups of radars is within the tolerance range of the sorting algorithm, when the two signal center values are not of the same order of magnitude, the difference is more than 10 times, and the signal variation is less than 30% of the center value, the sorting error of the algorithm for the radar signal is at least 25%. The sorting algorithm fails to sort signals. At the same time, for the sorting failure of the staggered signal, the sub-PRI design formula of the staggered signal is proposed, and the staggered signal satisfying the design formula can make the sorting algorithm invalid. Finally, the correctness of the SDIF failure principle is further verified by formula derivation, signal design simulation and experiment. The principle of sorting failure provides theoretical support and foundation for the design of anti-sorting RF stealth signal. The principle of sorting failure makes up for the shortcomings of random signal design and improves the design efficiency of RF stealth signal.

Radio frequency (RF) stealth is one of the essential research hotspots in the radar field. The anti-sorting signal is an important direction of the RF stealth signal. Theoretically speaking, the anti-sorting signal design is based on the failure principle of the radar signal sorting algorithm, and the SDIF algorithm is a core sorting algorithm widely used in engineering. Thus, in this paper, the SDIF algorithm is first analyzed in detail. It is pointed out that the threshold function of the SDIF algorithm will fail when the signal pulse repetition interval (PRI) value obeys the interval distribution whose length is 20 times larger than the minimum interval of PRI. Secondly, the correctness of the failure principle of SDIF threshold separation is proved by the formula. Finally, the correctness is further verified by the signal design case. The principle of SDIF sorting threshold failure provides theoretical support for anti-sorting RF stealth signal design. It also complements the shortcoming of the casual design for the anti-sorting signal. Furthermore, the principle of SDIF sorting threshold failure helps improve anti-sorting signal design efficiency. Compared with the Dwell & Switch (D&S) signal and jitter signal, the anti-sorting ability of the signal designed by using the sorting failure principle is notably enhanced through simulation and experimentation.

Content-based image retrieval (CBIR) has been widely used in many applications. Large storage and computation overheads have made the outsourcing of CBIR services attractive. However, the privacy issues brought by outsourcing have become a big problem. In this paper, a secure CBIR scheme based on an encrypted difference histogram (EDH-CBIR) is proposed. Firstly, the image owner calculates the order or disorder difference matrices of RGB components and encrypts them by value replacement and position scrambling. The encrypted images are then uploaded to the cloud server who extracts encrypted difference histograms as image feature vectors. To search similar images, the query image is encrypted by the image users as the image owner does, and the query feature vector is extracted by the cloud server. The Euclidean distance between query feature vector and image feature vector is calculated to measure the similarity. The security analysis and experiments demonstrate the usability of the proposed scheme.