With the increasing number of items requiring handling simultaneously in complex logistics, offline three-dimensional packing methods need to plan larger numbers of items. Existing deep reinforcement learning (DRL)-based packing methods cannot plan for large numbers of items while keeping high-quality solutions due to limited exploration space and high computational complexity. To address this issue, this paper proposes a scalable DRL-based packing method. An attention-based pack-Q-network (PQNet) is constructed to learn the optimal packing policy by integrating unpacked items, available spaces, and packed items. To expand the valid exploration space, a bidding-based multi-policy (BBMP) framework composed of multiple PQNets is designed to efficiently explore more latent valid solutions, thus enhancing solution quality. To reduce computational complexity, a training-free dynamic candidate selection (DCS) framework is proposed to incorporate comprehensive item information during execution with minimal computation overhead, which helps in effectively planning large numbers of items. Experimental results show that across item numbers of 20~1000, our method consistently outperforms the best-performing baseline at each tested scale by 3.2%~13.1% in space utilization.

• Content-adaptive video encryption in the compressed domain • Dynamically selects syntax elements based on video content complexity • Maintains full format compliance using Adaptive Syntax Integrity (ASI) • Tunable parameters balance encryption strength and bitrate increase With the ever-increasing amount of digital video content, efficient encryption is crucial to protect visual content across diverse platforms. Existing methods often incur excessive bitrate overhead due to content variability. Furthermore, since most videos are already compressed, encryption in the compressed domain is essential to avoid processing overhead and re-compression quality loss. However, achieving both format compliance and compression efficiency while ensuring that the decoded content remains unrecognizable is challenging in the compressed domain, since only limited information is available without full decoding. This paper proposes an adaptive compressed domain video encryption (ACDC) method that dynamically adjusts the encryption strategy according to content characteristics. Two tunable parameters derived from the bitstream information enable adaptation to various application requirements. An adaptive syntax integrity method is employed to produce format-compliant bitstreams without full decoding. Experimental results show that ACDC reduces bitrate overhead by 48.2% and achieves a 31-fold speedup in encryption time compared to the latest state of the art, while producing visually unrecognizable outputs.

A verifiable privacy-preserving image retrieval scheme provides an effective approach for resource-constrained IoT devices to achieve secure image retrieval in untrusted cloud environments. However, existing schemes based on data structures still rely on verification structures provided by the cloud server during the verification process, introducing fundamental trust deficiencies. While blockchain-based schemes can ensure trustworthy results, they incur significant storage and computational overhead by undertaking the entire retrieval task. To overcome these limitations, a blockchain-assisted verifiable privacy-preserving image retrieval scheme in IoT environment is proposed in this paper. By adopting an innovative architecture where the retrieval task is retained by the cloud server and the verification task is delegated to the blockchain, a trusted verification mechanism independent of the cloud server is constructed. Specifically, an inverted indexing generation method based on piece-wise mean quantization is designed, enabling the blockchain to efficiently verify the completeness of the cloud server’s retrieval. A metadata generation method leveraging Pedersen commitments is developed to support batch integrity verification of retrieval results by users. Security analysis and experimental results demonstrate that our scheme satisfies security prerequisites and excels beyond existing solutions in terms of retrieval accuracy, search efficiency, verification efficiency, and storage overhead. On the real-world dataset, compared with existing scheme, our scheme demonstrates an 82% enhancement in search efficiency, a 36% improvement in verification efficiency, along with a 37% reduction in storage overhead.

The existing cloud-storage image integrity verification (CSIIV) schemes typically assume that the cloud server is untrusted while the user is trusted. This one-way trust model renders these schemes incapable of defending against malicious users who may forge authentication information or manipulate the verification process. To address these issues, this paper proposes a blockchain-based efficient and trusted cloud-stored image integrity verification scheme. The scheme explicitly requires resistance against tampering by the cloud server and false accusations by the user, thereby establishing a bidirectional trusted verification model. Specifically, a robust metadata generation and storage method based on blockchain is designed. By employing a differential hashing algorithm, metadata robust to common image noise yet sensitive to malicious tampering are generated, and blockchain is utilized for distributed storage. This approach prevents user forgery at the source and overcomes the fragility of metadata. Additionally, a verification dispute arbitration mechanism based on smart contracts is developed. When a verification dispute occurs, this mechanism determines the responsible party based on the on-chain metadata, achieving fair arbitration and ensuring the trustworthiness of the entire verification process. Theoretical analysis and experimental results demonstrate that the proposed scheme meets security requirements while significantly improving verification efficiency. On images from the real-world Set12 dataset, the verification efficiency is improved by 77%, 18% and 38%, respectively, compared with existing CSIIV schemes.

To enhance the embedding capacity and security of reversible data hiding in encrypted images (RDHEI), an RDHEI algorithm based on variational lossless compression (VLC) and dual encryption is proposed. Firstly, VLC model is introduced to ensure reversibility while achieving high embedding capacity. On the basis of variational autoencoder model, the VLC model incorporates an error encoding module to record the difference between the input and output images, thereby achieving lossless compression. Subsequently, dual encryption includes both stream encryption and model encryption, ensuring security both in the carrier and method, and original images necessitate dual decryption for accurate recovery. Experimental results demonstrate that the proposed algorithm elevates embedding rate by at least 0.12 bpp in UCID compared to existing algorithms, and can resist brute force attacks to achieve high security.

The online setting brings greater flexibility and practicality to the three-dimensional bin packing problem (3DBPP) but at the cost of algorithm performance. Existing methods mitigate the performance impact by introducing semionline settings with look-ahead or buffer zones. However, these methods either fail to fundamentally alter the packing order or reduce packing efficiency. This paper proposes a novel semionline setting that allows for the observation of multiple items and the selection of one for packing, thereby adjusting the packing order without reducing packing efficiency. We do work for solving the semi-online packing problem via reinforcement learning which faces two real-world challenges: (1) a variable and difficult-to-predict number of observed items, and (2) the obstruction of robotic arm movement by already packed items. On the one hand, we design a policy network capable of adapting to variable item quantities. On the other hand, we introduce a guided bottom-up packing reward function to free up space for robotic arm motion. We show that our method outperforms the baselines in terms of space utilization with the condition of observing at least two items. Further experiments demonstrate the functionality of our reward function, which can guide a virtual robot to complete packing tasks.

The increasing demands of modern logistics have driven the need for the development of efficient packing methods capable of addressing the 3D Packing Problem (3D-PP). While Deep Reinforcement Learning (DRL) has emerged as a promising solution, the conventional three-stage scheme used in existing DRL-based methods still faces challenges, particularly in coordinating the behaviors of its constituent sub-networks and managing the large action space for item placement on instances involving large-sized bins. This work proposes a two-stage scheme to integrate item index and orientation selections into a single sub-stage, thereby simplifying behavioral coordination. To mitigate the issue of excessive memory usage associated with the selection integration, a Set Transformer with Induced Set Attention Block (ISAB) is employed to encode the rotated item state, thus keeping relatively light computation. Additionally, we propose a bidirectional packing method that compresses the placement action space while encouraging the agent to explore reasonable placement positions. Experimental results demonstrate that the Two-Stage Bidirectional Packing (TS-BP) framework, formed by the above components, improves space utilization of 2.8%∼4.6% on high-difficulty packing instances compared to current state-of-the-art methods. The code is available at https://github.com/Ashenone511/Two-Stage-Bidirectional-Packing-Framework.

In cloud environments, privacy-preserving contentbased image retrieval (PPCBIR) enables users to retrieve images while protecting image privacy. Existing PPCBIR systems often use a single image key, which causes low efficiency and makes it difficult to achieve fine-grained access control over images. This paper proposes a lightweight and controllable privacy-preserving image retrieval in multi-user settings (named LCPIRM) to improve time efficiency and access control performance. A one-time image encryption method based on reversible embedding is proposed to balance the contradiction between complexity and security without increasing the difficulty of key management. A robust hash generation method is designed by combining piecewise mean quantization and encryption image features, which can effectively improve retrieval efficiency because the robust hashes embedded in the encrypted images can be extracted and establish inverted indexing in the cloud. When dealing with authorized encrypted images, the cloud server uses proxy re-encryption to convert the image keys embedded within themselves from the owner's public key protection to the authorized user's public key protection, achieving fine-grained access control over images in a multi-user setting. Theoretical analysis and experimental results show that LCPIRM has better performance in terms of retrieval accuracy, consumption, and search efficiency while meeting security requirements. In the real datasets Caltech256 and Caltech101, the search efficiency has increased by 74% and 58% respectively compared to the existing schemes.

Reversible data hiding in encrypted images (RDHEI) has been recognized as an effective method for overcoming management difficulties within picture archiving and communication system (PACS). However, most existing RDHEI algorithms still encounter notable challenges when applied to the PACS, specifically in terms of their key management, embedding capacity, and security. This paper introduces a novel framework and corresponding algorithm for reversible data hiding in encrypted medical images (RDHEMI) to bridge this gap. The framework employs a unique key for each patient and maintains consistency in the key linked to patient images regardless of changes in doctor, thereby addressing key management challenges. In the proposed algorithm, Huffman tree coding (HTC) integrates Huffman coding with innovative leaf-to-leaf coding, achieving a better compression performance for medical images than move-to-front (MTF) cache and Huffman coding, as medical images contain more smooth areas. Count-encryption (CE) produces encryption keys according to the frequency of encryption occurrences for an image and ensures a peak signal-to-noise ratio under 8 dB for multiple encryptions with the same key, enhancing the algorithm’s resistance to attacks. The experimental results demonstrate that the proposed algorithm achieves high security to counter various attacks and outperforms existing algorithms in terms of the time complexity and embedding capacity, with an improvement of 0.21 bpp.

For the security risks and high transmission/storage consumption in cloud-based medical images storage systems (CMISS), reversible data hiding in encrypted images (RDHEI) provide an effective solution. Nevertheless, challenges persist concerning the security risks cause by key transmission and the large file size of encrypted medical images. Consequently, a cloud-based privacy-preserving medical images storage scheme with low consumption is proposed in this paper. First, RDHEI is applied to CMISS, where image encryption achieves privacy protection, reversible data hiding eliminates extra space consumption by index data self-hiding, and the reversibility enables lossless recovery and extraction of medical images and index data. Then, hybrid encryption is designed to achieve high security. The security of encrypted images is guaranteed by combining a one-time cryptosystem with symmetric XOR encryption, which makes our scheme can resist various attacks. Time-varying key used in XOR is encrypted by asymmetric RSA, and only public key is used in RSA, avoiding the risk of private key transmission. Finally, to reduce the file size of encrypted images and achieve low consumption, context Huffman coding is proposed to adaptively selects the block coding method by context and thresholds, and has at most 98 056 bits shorter than Huffman coding in encoded stream length. Experimental results show that the proposed scheme has better performance in terms on security, consumption, and reversibility. The minimum compression ratio in databases is 32.46%, which is 2.63% lower than the existing schemes. And the medical image and index data can be restored lossless.

Existing JPEG encryption approaches pose a security risk due to the difficulty in changing all block-feature values while considering format compatibility and file size expansion. To address these concerns, this paper introduces a novel JPEG image encryption scheme. First, the security of sketch information against chosen-plaintext attacks is improved by increasing the change rate of block-feature values. Second, a classification global permutation approach is designed to encrypt the undivided run/size, value (RSV)-based AC groups to achieve larger changes in the block-feature values. Third, to reduce file size expansion while maintaining format compatibility, the DC coefficients are rotated based on the mapped DC differences in the same category, and the nonzero AC coefficients are mapped in the same category. Extensive experiments demonstrate that the proposed algorithm is superior to existing schemes in terms of security. Notably, the average change rate of block-feature values is increased by at least 20%. Furthermore, the proposed scheme reduces the file size by an average of 2.036% compared to existing JPEG image encryption 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.

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.

In the existing JPEG image encryption schemes , the block feature values that can be used to reduce key search space are either difficult to be changed or need to be changed through overflow processing which leads to low generality and high encryption runtime. To change block feature values without overflow processing, a novel JPEG image encryption scheme is proposed. For AC encryption, the complete and end AC groups based on undivided RSV (run/size, value) (ACG-URSV) are permuted separately to change different features. Complete ACG-URSV containing different number of RSVs is used to change the non-zero coefficients count (NCC) and energy of AC coefficients (EAC). End ACG-URSV containing the zero coefficients after position of last non-zero AC coefficient (PLZ) is mainly used to change PLZ. Besides, the intra-block RSV permutation and block permutation are used to further destroy correlation. For DC encryption, the positive DC prediction error (PDC-PE) groups modulo encryption is proposed to avoid overflow processing. The experimental results show that this paper reduces encryption runtime by more than half, improves generality by at least 20%. When quality factor of 90, the average change rates of NCC, EAC and PLZ values are increased by 96.28%, 11.68% and 29.15%, respectively.

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.

Reversible data hiding in encrypted images (RDHEI) technique can be used to realize privacy protection and management in the image outsourcing scenario. Most existing RDHEI schemes focus on increasing the maximum embedding rate (Max-ER), but not paying much attention to the security improvement under various attacks. In this paper, a RDHEI method based on the adaptive bit-plane (ABP) coding is proposed to improve the Max-ER. The order-index extended scrambling (OIES) encryption scheme is also developed to strengthen the RDHEI's ability of thwarting various attacks. The effectiveness of ABP coding is achieved by proper selections of the threshold. The OIES enables the design of a novel scramble-key (SK) generation method to greatly reduce the probability of generating the same SK by the same user-key. This significantly improves the ability of resisting various attacks in that the attack on the scrambling encryption is mainly via the SK rather than the user-key estimation. Analysis shows that the probability of OIES obtaining the same SK is reduced from 1.0 to 0.01 for different images and to 1/2αfor the same image. Simulation results demonstrate that the proposed ABP coding and OIES schemes outperform the state-of-the-art RDHEI algorithms in terms of the Max-ER and ability against various attacks.

Image privacy protection and management face many challenges, such as privacy disclosure, copyright dispute, and traceability difficulties, with the development of big data. Reversible data hiding in encrypted images (RDHEI) has been widely considered as an effective means to tackle these challenges. In this paper, a RDHEI based on time-varying Huffman coding table (TV-HCT) method is proposed to improve the security, embedding rate (ER) and efficiency. First, the initial HCT is generated according to the prediction errors of an image, which can improve compression performance. And then, the TV-HCT is obtained by scrambling equal-length codewords in the initial HCT using timestamps. This realizes the time variability of compression coding stream (CCS) of an image in that the image TV-HCT has large change space. Analysis shows that the average change space of TV-HCT in UCID is 3.97×10327, and the average ER of three databases is more than 0.44 bpp higher than the existing algorithms. Finally, the CCS is encrypted using the designed index class scrambling method to balance complexity and security. The proposed method not only strengthens the security against brute force attack and differential attack, but also improves ER and efficiency of the RDHEI technique. Experimental results and performance analysis demonstrate that the proposed algorithm outperforms the state-of-the-art RDHEI algorithms in terms of the security, ER and complexity.

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.

Adversarial learning stability has an important influence on the generated image quality and convergence process in generative adversarial networks (GANs). Training dataset (real data) noise and the balance of game players have an impact on adversarial learning stability. In the gradient backpropagation of the discriminator, the noise samples increase the gradient variance. It can increase the uncertainty in the network convergence progress and affect stability. In the two-player zero-sum game, the game ability of the generator and discriminator is unbalanced. Generally, the game ability of the generator is weaker than that of the discriminator, which affects the stability. To improve the stability, an antinoise learning and coalitional game generative adversarial network (ANL-CG GAN) is proposed, which achieves this goal through the following two strategies. (i) In the real data loss function of the discriminator, an effective antinoise learning method is designed, which can improve the gradient variance and network convergence uncertainty. (ii) In the zero-sum game, a generator coalitional game module is designed to enhance its game ability, which can improve the balance between the generator and discriminator via a coalitional game strategy. To verify the performance of this model, the generated results of the designed GAN and other GAN models in CELEBA and CIFAR10 are compared and analyzed. Experimental results show that the novel GAN can improve adversarial learning stability, generate image quality, and reduce the number of training epochs.

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.

Generative adversarial network (GAN) is the most important natural image synthesis method, and one of GAN major challenges is the adversarial learning stability. Usually, GAN directly optimizes a single divergence via the two-player zero-sum game of generator and discriminator. Because the parameter updating of generator and discriminator is complex and changeable, directly optimizing the single divergence is easy to fall into local optimal solution, which affects the adversarial learning stability. To improve the adversarial learning stability and convergence accuracy, the same solution constraints GAN (SSC-GAN) is proposed. In this novel GAN, a first-order ordinary differential equation (ODE) is constructed by training set probability density function, which is the same solution problem of GAN optimization problem, and its constraint conditions are given to ensure the existence and uniqueness of the ODE solution. These constraints are used to guide the GAN parameter updating to improve the adversarial learning stability, thereby getting better training effects. In CELEBA and CIFAR10, the experimental results show that the novel GAN is better than the classic GAN models. In CELEBA, CIFAR10 and LSUN-BEDROOM, the proposed SSC-GAN is also better than two state-of-the-art GAN models, SAGAN and SNGAN.

The reversible data hiding in encrypted images (RDH-EI) technique has been widely used to achieve privacy protection and convenient management of cloud storage images. The existing RDH-EI methods focus on the embedding capacity, reversibility, and the quality of decrypted images. This paper considers a new performance indicator of RDH-EI, the file size of encrypted images, which directly affects the cloud storage cost of content owner and network transmission efficiency. The goal is to obtain the smallest file size and to vacate sufficient space for data embedding. We propose a multi-MSB compression method that includes three strategies: the iterative MSBs-inversion prediction (IMIP), the adjacent prediction plane XOR (APPX), and the block variable length coding (BVLC). The file size of encrypted images is adaptively minimized while ensuring the format compatibility and lossless reconstruction. An encrypted image is generated by a combination of stream ciphers and bit scrambling to improve the resistance to various attacks such as the cipher-only attack (COA). Experimental results demonstrate that the proposed method has higher embedding capacity and resistance to COA compared with the current state-of-the-art methods for images with different texture complexity. The proposed algorithm can achieve the maximum embedding rate ranging from 0.74 to 4.78 bpp.

Inshore ship detection from high-resolution satellite images is a useful yet challenging task in remote surveillance and military reconnaissance. It is difficult to detect the inshore ships with high precision because various interferences are present in the harbor scene. An inshore ship detection method based on the weighted voting and rotation-scale-invariant pose is proposed to improve the detection performance. The proposed method defines the rotation angle pose and the scaling factor of the detected ship to detect the ship with different directions and different sizes. For each pixel on the ship template, the possible poses of a detection window are estimated according to all possible pose-related pixels. To improve robustness to the shape-similar distractor and various interferences, the score of the detection window is obtained by designing a pose weighted voting method. Moreover, the values of some parameters such as similarity threshold and the weight of “V” are investigated. The experimental results on actual satellite images demonstrate that the proposed method is invariant to rotation and scale and robust in the inshore ship detection. In addition, better detection performance is observed in comparison with the existing inshore ship detection algorithms in terms of precision rate and recall rate. The target pose of the detected ship can also be obtained as a byproduct of the ship detection.

To improve the detection precision in complicated backgrounds, a novel rotation-invariant object detection method to detect objects in remote sensing images is proposed in this letter. First, a rotation-invariant feature called radial-gradient angle (RGA) is defined and used to find potential object pixels from the detected image blocks by combining with radial distance. Then, a principal direction voting process is proposed to gather the evidence of objects from potential object pixels. Since the RGA combined with the radial distance is discriminative and the voting process gathers the evidence of objects independently, the interference of the backgrounds is effectively reduced. Experimental results demonstrate that the proposed method outperforms other existing well-known methods (such as the shape context-based method and rotation-invariant part-based model) and achieves higher detection precision for objects with different directions and shapes in complicated background. Moreover, the antinoise performance and parameter influence are also discussed.

This paper proposes a color-direction patch sparsity-based image in painting method to better maintain structure coherence, texture clarity, and neighborhood consistence of the in painted region of an image. The method uses super-wavelet transform to estimate the multi-direction features of a degraded image, and combines with color information to construct the weighted color-direction distance (WCDD) to measure the difference between two patches. Based on the WCDD, the color-direction structure sparsity is defined to obtain a more robust filling order and more suitable multiple candidate patches are searched. Then, the target patches are sparsely represented by the multiple candidate patches under neighborhood consistency constraints in both the color and the multi-direction spaces. Experimental results are presented to demonstrate the effectiveness of the proposed approach on tasks such as scratch removal, text removal, block removal, and object removal. The effects of super-wavelet transforms and direction features are also investigated.

In this paper, we present the performance analysis of a self-recovery fragile watermarking scheme using block-neighbor- hood tamper characterization. This method uses a pseudorandom sequence to generate the nonlinear block-mapping and employs an optimized neighborhood characterization method to detect the tampering. Performance of the proposed method and its resistance to malicious attacks are analyzed. We also investigate three optimization strategies that will further improve the quality of tamper localization and recovery. Simulation results demonstrate that the proposed method allows image recovery with an acceptable visual quality (peak signal-to-noise ratio (PSNR) as 25 dB) up to 60% tampering.