This paper investigates the differential privacy consensus problem for a class of multiagent systems under dynamic topologies. To meet the requirements of power consumption, a random communication strategy is proposed in which each agent sends data to its neighbors with different probabilities. For analyzing the effect of time-varying topology and coupling strength among agents on system performance, a necessary and sufficient condition for almost sure convergence of differential privacy consensus systems is established. Furthermore, the convergence rate and convergence accuracy of the system are also studied. By formulating the communication costs and topological characteristics as a constrained problem, a convex optimization algorithm for fast convergence of the differential privacy consensus system is proposed. In addition, the differential privacy of the agents is analyzed, and the optimal noise parameters that achieve a trade-off between convergence accuracy and privacy levels are derived. A numerical simulation is presented to demonstrate the effectiveness of the developed approach.

Packet classification is crucial for network security, traffic management, and quality of service by enabling efficient identification and handling of data packets. Decision tree-based rule partitioning has emerged as a prominent method in recent research. A significant challenge for decision tree algorithms is rule replication, which occurs when rules span multiple subspaces, leading to substantial memory consumption increases. Rule partitioning can effectively mitigate or eliminate this replication by separating overlapping rules. However, existing partitioning techniques heavily rely on manual parameter tuning across a wide range of possible values, making optimal solution discovery challenging. Furthermore, due to the lack of global optimization, these approaches face a critical trade-off: either the number of subsets becomes uncontrollable, resulting in diminished query speed, or rule replication becomes severe, causing substantial memory overhead. To bridge these gaps and achieve high-performance adaptive partitioning, we propose ERPC, a novel algorithm with the following key features: First, ERPC leverages graph theory to model rule sets, enabling global optimization that balances intra-group rule replication against the total number of groups. Second, ERPC advances rule set partitioning by modifying traditional community detection algorithms, strategically shifting the optimization objective from positive to negative modularity. Third, ERPC allows the rule set itself to determine the optimal number of groups, thus eliminating the need for manual parameter tuning. Experimental results demonstrate the efficacy of ERPC when applied to CutSplit, a state-of-the-art multi-tree method. It preserves 88% of CutSplit’s average classification throughput while reducing tree-building time by 89% and memory consumption by 77%. Furthermore, ERPC exhibits strong scalability, being adaptable to mainstream decision tree methods.

Reversible data hiding in encrypted images (RDH-EI) has gained widespread attention due to its potential applications in secure cloud storage. However, the security challenges of RDH-EI in cloud storage scenarios remain largely unexplored. In this article, we present a counterfeiting attack on RDH-EI schemes that utilize block-permutation and Co-XOR (BPCX) encryption. We demonstrate that ciphertext images generated by BPCX-based RDH-EI are easily tampered with to produce a counterfeit decrypted image with different contents imperceptible to the human eye. This vulnerability is mainly because the block permutation key information of BPCX is susceptible to known-plaintext attacks (KPAs). Taking ciphertext images in telemedicine scenarios as an example, we describe two potential counterfeiting attacks, namely fixed-area and optimal-area attacks. We show that the quality of forged decrypted images depends on the accuracy of the estimated block-permutation key under KPA conditions. To improve the invisibility of counterfeit decrypted images, we analyze the limitations of existing KPA methods against BPCX encryption for \(2\times 2\) block sizes and propose a novel diagonal inversion rule specifically designed for image blocks. This rule further enhances the accuracy of the estimated block-permutation key. The experiments show that, compared to existing KPA methods, the accuracy of the estimated block-permutation key in the UCID dataset increases by an average of 11.5%. In the counterfeiting attack experiments on Camera’s encrypted image, we successfully tampered with over 80% of the pixels in the target area under the fixed-region attack. Additionally, we achieved a tampering success rate exceeding 90% in the optimal-region attack.

The optimization of video compression and streaming workflows critically relies on understanding the video complexity, including both spatial and temporal features. These features play a vital role in guiding rate control, predicting video encoding parameters (such as resolution and frame rate), and selecting test videos for subjective analysis. Traditional methods primarily utilize Spatial Information (SI) and Temporal Information (TI) to measure these spatial and temporal complexity features, respectively. Moreover, the Video Complexity Analyzer (VCA) has been introduced as a tool employing Discrete Cosine Transform (DCT)-based functions, namely E and h, to evaluate the spatial and temporal complexity features, respectively. In this paper, we introduce the Enhanced Video Complexity Analyzer (EVCA), an advanced tool that integrates the functionalities of both VCA and the SITI approach. Developed in Python to ensure compatibility with GPU processing, EVCA enhances the definition of temporal complexity originally used in VCA. This refinement significantly improves the detection of temporal complexity features in VCA (i.e., h), raising its Pearson Correlation Coefficient (PCC) from 0.6 to 0.77. Furthermore, EVCA demonstrates exceptional performance on Graphics Processing Unit (GPU) devices, achieving feature extraction speeds exceeding 1200 fps for 1080p resolution videos.

As video dimensions – including resolution, frame rate, and bit depth – increase, a larger bitrate is required to maintain a higher Quality of Experience (QoE). While videos are often optimized for resolution and frame rate to improve compression and energy efficiency, the impact of color space is often overlooked. Larger color spaces are essential for avoiding color banding and delivering High Dynamic Range (HDR) content with richer, more accurate colors, although this comes at the cost of higher processing energy. This paper investigates the effects of bit depth and color subsampling on video compression efficiency and energy consumption. By analyzing different bit depths and subsampling schemes, we aim to determine optimized settings that balance compression efficiency with energy consumption, ultimately contributing to more sustainable and high-quality video delivery. We evaluate both encoding and decoding energy consumption and assess the quality of videos using various metrics including PSNR, VMAF, ColorVideoVDP, and CAMBI. Our findings offer valuable insights for video codec developers and content providers aiming to improve the performance and environmental footprint of their video streaming services.

With the proliferation of cloud applications, users are increasingly uploading their private images to cloud servers to avail of supplement storage capacity. Unlike image encryption, the thumbnail-preserving technique is a way of safeguarding image privacy and preserving the outline of the thumbnail image, which allows authorized users to recognize it according to prior knowledge of the original image. Recently, several thumbnail-preserving encryption schemes have been proposed. But most of the sum-preserving encryption-based schemes are irreversible, thereby significantly constraining their practical value. Moreover, the visual quality of images generated with the reversible thumbnail-preserving schemes is unsatisfactory, leading to a decrease in the user recognition accuracy. In light of the aforementioned, this paper presents an efficient scheme to generate thumbnail-preserving images with high visual naturalness, while ensuring reversibility of the process. Experimental results demonstrate that the proposed scheme achieves significant visual quality improvements compared to state-of-the-art schemes. Furthermore, the thumbnail-preserving images generated by using our scheme can effectively evade detection by confusing malicious attackers.

With the development of Internet of Things (IoT) and cloud services, many images generated from IoT devices are stored in the cloud, calling for efficient data encryption methods. To balance the security and usability, the thumbnail preserving encryption (TPE) has emerged. However, existing JPEG image-based TPE (JPEG-TPE) schemes face challenges in achieving low file extension, lossless decryption and better privacy protect of detailed information. To solve these challenges, we propose a novel JPEG-TPE scheme. Firstly, to achieve a smaller file size expansion and preserve the thumbnail, we reallocate the values, maintaining the sum for the DC difference instead of the DC coefficient. To ensure that the coefficients do not overflow, the valid range of reallocated difference is constrained not only by the sum but also by the neighborhood difference. Secondly, to preserve file size of AC encryption while improve the security of detailed information, the AC coefficient groups with undivided RSV are permuted adaptively. Besides, the intra TPE block swapping of DC difference, quantization table modification, non-zero AC coefficients mapping, and block permutation are used to further encrypt the image. The experimental results show that the proposed JPEG-TPE scheme achieves lossless decryption, reducing the file size expansion of encrypted images from 15.41% to 0.64% compared to the state-of-the-art scheme. Additionally, it is observed that the proposed method can effectively resist against various attacks, including the deep-learning based super-resolution attack.

In this paper, we propose a novel attack model called the Got Plaintext Attack (GPA), where the attacker only requires one plaintext and the ciphertext image set stored in the cloud to attack the content of the ciphertext image. Using this model, we examine the security of the Improved Redundant Space Transfer (IRST) encryption method. To this end, we define an ordered characteristic matrix based on the properties of the three keys used in IRST. By comparing the histogram distance of the ordered characteristic matrix, we are able to obtain a plain-ciphertext pair. Furthermore, by leveraging the invariant properties of the ordered characteristic matrix of image blocks in the plain-ciphertext pair, we estimate the block permutation Π2and the bit-plane permutation sequence Π1. Our experiments show that the accuracy of estimating Π2is higher than 70% for block sizes of 3x3 pixels or larger. Despite a 40% accuracy in estimating Π1, the content information of the ciphertext image can still be exposed.

JPEG images are widely used on the Internet. Histogram shifting reversible data hiding (RDH) methods based on quantized DCT (discrete cosine transform) coefficients are a research focus for JPEG images. Among them, frequency band selection is a key step that affects the performance of JPEG image RDH. In the existing algorithm, frequency band selection is to evaluate the embedding perform ance of the whole frequency band. But after DCT block sorting, the distribution of expand AC (alternating current) coefficients (Coefficients that can carry data) is in the front of the frequency band, so the performance evaluation of the whole frequency band will produce errors. This paper proposed an adaptive frequency band length JPEG image RDH method, which determines the used length of the frequency band while selecting the frequency band, so that can effectively reduce the invalid shift. Instead of selecting the whole frequency band length in a fixed mode for performance evaluation, the optimal combination of frequency band lengths will be selected according to the embedding performance of different lengths of each frequency band. Then, a united solution mechanism is used to solve the optimal frequency band length, and the frequency band selection is also completed while solving the frequency band length. Experimental results show that our algorithm outperforms existing state-of-the-art methods in terms of marked image visual quality and file size increment.

This paper introduces an algorithm to improve the security, efficiency, and embedding capacity of reversible data hiding in encrypted images (RDH-EI). It is based on classification encryption difference and adaptive fixed-length coding. Firstly, the prediction error image is obtained, the difference with a bin value greater than the encryption threshold in the difference histogram is found, and it is further modified to obtain the embedding threshold range. Then, under the condition of ensuring that the difference inside and outside the embedding threshold range is not confused, the difference within the threshold is only scrambled, and the difference outside the threshold is scrambled and mod encrypted. After obtaining the encrypted image, an adaptive difference fixed-length coding method is proposed to encode and compress the differences within the threshold. The secret data is embedded in the multiple most significant bits of the encoded difference. Experimental results show that the embedding capacity of the proposed algorithm is improved compared with the state-of-the-art algorithm.

Block permutation and Co-XOR (BPCX) image encryption is a commonly used encryption method for reversible data hiding in the encryption domain, which can effectively improve the embedded capacity and the ability resisting the existing attacks including ciphertext-only attack and known plaintext attack (KPA). This paper proposes a KPA based on bit-block inversion and mean equivalent division (MED) to estimate the block permutation key of BPCX image encryption. Firstly, we divide an image block into the bit-block and point out that the maximum of the numbers of 0 bit and 1 bit of a bit-block before and after the Co-XOR encryption remains unchanged. And then two inversion rules of bit-block are defined to construct pseudo plain-ciphertext images to eliminate the influence of pixel value changes caused by Co-XOR encryption. Finally, the MED based KPA is designed to estimate the block permutation key sequence according to the pseudo plain-ciphertext images. The relationship between the key estimation accuracy and the number of known plain-ciphertext pairs, block size, and pseudo ciphertext are discussed. Experimental results show that even in the minimum block size ($2\times 2$), the average estimated correct rate of the block permutation sequence exceeds 40%. The block permutation key estimation accuracy is more than 50% when the block size is greater than$3\times 3$. Some improved encryption methods against the proposed KPA are also given.

Reversible data hiding in encrypted images (RDH-EI) technology is commonly used in cloud storage images for privacy protection. Most existing RDH-EI techniques reported in the literature applied block permutation and co-modulation (BPCM) encryption to generate encrypted images. This work analyses the security of the RDH-EI algorithm based on BPCM encryption under known plaintext attacks (KPAs). Different from the existing KPAs, this paper considers that attackers can perform KPAs based on marked encrypted images and shows that BPCM encryption has the risk of information leakage. To find the constant features of a block before and after co-modulation, the first-pixel difference block (FDB) of a block is first defined. Then, a pseudo cypher difference image of the cyphertext image is constructed to eliminate the changed FDBs so that the differences in the cyphertext FDBs are the same as the FDBs in the corresponding plaintext difference image. Finally, we design an FDB-based block permutation key estimation method according to the plaintext difference image and pseudocyphertext difference image. The influence of block size on key estimation accuracy and the time complexity of the proposed KPA algorithm are analysed and discussed. Experimental results show that the correct rate of key estimation is positively correlated with the block size and the number of plain-cyphertext pairs. The average correct rate of key estimation reaches 63% when the block size is greater than 3×3.