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.

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.

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.

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.

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. Github: https://github.com/cd-athena/EVCA