The rise of IoT devices in daily life has led to massive amounts of sensitive data. Efficient and secure storage and processing of this data is key to unlocking IoT’s potential. Although homomorphic encryption (HE) ensures privacy and supports efficient and complex private data processing algorithms, its high computational and communication costs during encryption pose challenges for resource-limited IoT devices. We propose a novel framework to reduce the encryption and upload burden on IoT devices by enabling seamless conversion from lightweight secret shares to HE ciphertexts under the semi-honest model. Our design introduces a new secret sharing scheme and leverages efficient pseudorandom functions (PRFs) combined with private-key HE ciphertexts to improve reconstruction efficiency. After computation, results can be converted back into secret shares, supporting bidirectional transformation between HE ciphertexts and secret shares. Unlike existing approaches, our method eliminates reliance on specific HE parameters, allowing better integration with current HE-based privacy-preserving protocols. It also offers a more suitable solution for long-term storage of sensitive data, such as health records and images.

Learning adaptive embedding costs with deep learning has become an important direction for improving steganographic security. However, most existing approaches focus on spatial-domain images, while effective cost learning for JPEG images remains challenging due to the complexity introduced by block-wise discrete cosine transform and lossy quantization. In this paper, we present an enhanced GAN-based framework that learns asymmetric embedding costs for JPEG images from scratch, improving both content adaptivity and training stability. Specifically, we introduce an adaptive modification loss that directly links the generator’s predicted embedding probabilities to the actual modification behavior, enabling fully data-driven and content-aware optimization without relying on handcrafted filters or quantization-dependent heuristics. In addition, we propose a random masking strategy applied during later training stages to prevent discriminator dominance and maintain informative adversarial feedback. Extensive experiments on multiple benchmark datasets and under various steganalytic detectors demonstrate that the proposed method consistently improves steganographic security over existing JPEG-domain approaches. Ablation studies further validate the effectiveness of the proposed loss design and training strategy.

Moving Object Detection (MOD) can be outsourced to the cloud for computational convenience, in which case the video must be encrypted to protect privacy. Secure video MOD methods designed to perform MOD on encrypted video are still in their infancy. In this paper, we present an attention-based framework for privacy-preserving MOD in compressed videos. On the user side, we adopt selective video encryption for the compressed video, while in the cloud, we extract the Compressed Video entropy-coded Syntax Elements (CVSE) from the encrypted video. Since the extracted CVSE data lacks sufficient motion information and contains noise, we introduce a two-stage training process. In the first phase, we propose a new deep learning-based approach for interpolating CVSE-based motion feature maps, addressing a significant drawback of traditional methods that rely exclusively on empirical interpolation algorithms. In the second stage, we propose a specialized backbone tailored for feature extraction from sparse CVSE data. We then design an attention-based neck that focuses on areas with denser motion and varying sizes of moving objects. Experimental results on two public datasets, VIRAT and DUKE-MTMC, show that our framework achieves state-of-the-art detection performance. Compared to previous secure solutions, the proposed method exhibits more robustness in challenging scenes.

The increasing use of Voice over Internet Protocol (VoIP) technology in telecom fraud has become a serious global concern. Its ability to spoof caller IDs and IP addresses, and the use of overseas or anonymized servers make VoIP-based scams difficult to trace and regulate. As a result, distinguishing VoIP calls from conventional mobile phone calls based on voice signal characteristics is crucial for enhancing anti-fraud measures. However, existing forensic techniques often struggle to accurately identify speech transmitted via VoIP. To address this challenge, we propose a dual-level 1D-CNN that leverages both frame and utterance features for effective VoIP detection. After evaluating a range of acoustic features, we primarily focus on short-frame Mel-Frequency Cepstral Coefficients (MFCCs) due to their effectiveness in capturing VoIP characteristics. Given the frame-based processing and transmission nature of VoIP, we employ a 1D-CNN, rather than the more commonly used 2D-CNN that treats spectrograms as image, to extract frame-level codec features. Finally, we propose a dual-level classification strategy: the frame-level classifier captures encoding discrepancies within individual frames, while the utterance-level classifier aggregates these frame-level features to learn global encoding patterns through global covariance pooling. Experimental results on the VoIP Phone Call Identification Database (VPCID) demonstrate that the proposed method consistently outperforms existing approaches, delivering superior accuracy and robustness across a wide range of challenging scenarios. Moreover, comprehensive ablation studies validate the effectiveness and rationale behind the design of the proposed model architecture.

Centralized training of deep learning-based network intrusion detection systems (DLNIDS) raises privacy concerns and incurs huge overhead. Federated learning (FL), while preserving privacy, confronts challenges including size imbalance, heterogeneous and global imbalance. To address these issues, this paper proposes a Self-Balancing Federated learning Network Intrusion Detection System (SBFedNIDS) for accurate and efficient network intrusion detection while preserving privacy and tackling negative impact caused by multiple imbalances resulting from FL. SBFedNΠ)S can efficiently mitigate model bias cause by multiple imbalances and addresses imbalances at data-level via federated cost sensitive learning (FCSL) and client synthetic minority over-sampling technique (CSMOTE), without requiring additional training resources, unlike generative models. Extensive experiments on a benchmark dataset show that SBFedNIDS outperforms baselines in multiple imbalanced scenarios and even surpasses centralized training in several settings.

The explosive growth of multimedia data prompts resource-constrained users to outsource image storage and feature extraction (e.g., SURF) to cloud servers. To protect privacy, secure SURF outsourcing schemes have become a necessity. However, existing secure SURF outsourcing schemes generally suffer from drawbacks such as low efficiency and inaccurate feature extraction. To address these issues, we propose a secure SURF outsourcing scheme with improved integrity, accuracy, and efficiency. We decompose the complex steps of SURF into basic operations such as addition, multiplication, and comparison. A series of secret-sharing protocols are employed to enhance accuracy and efficiency, while securely implementing all SURF steps on the cloud. In addition, we design secure protocols for computing matrix determinants and absolute values, which support the scale space construction and descriptor generation in SURF, respectively. Extensive comparative experiments demonstrate that our scheme outperforms existing state-of-the-art approaches in terms of both accuracy and efficiency.

A key challenge in current steganography research is automatically learning image embedding costs without relying on existing costs. To date, there has been limited work on JPEG steganography, and the reported methods primarily rely on learning symmetric embedding, which fails to fully exploit the relationships between different modification directions within an embedding unit. This limitation restricts their security, leaving room for improvements in JPEG steganography techniques. To address this issue, we propose a GAN-based framework for JPEG steganography that learns asymmetric embedding costs from scratch. Our approach extends a modern framework of spatial steganography by incorporating a key IDCT module, which facilitates the conversion between JPEG and spatial domains. This enables the integration of effective spatial steganographic and steganalytic techniques into our framework for JPEG steganography. Additionally, we use a dual-branch UNet to generate separate embedding probabilities for +1 and -1 DCT coefficients and introduce a specialized loss function to guide DCT modifications. This loss function is designed by converting the modified DCT coefficients back to the spatial domain and analyzing various spatial residuals. Extensive experiments demonstrate that our method significantly outperforms existing JPEG steganography techniques, achieving state-of-the-art security performance. Furthermore, many ablation experiments validate the rationale of our model.

Cloud computing has become a crucial IT infrastructure in the big data era, with an increasing number of users outsourcing video storage and computing tasks to cloud servers, raising concerns about privacy leakage and illegal video distribution. Although existing robust video watermarking technologies can trace illegal distribution, they do not address privacy protection. This paper proposes a robust watermarking algorithm for protection of privacy in compressed video streams. We apply selective video encryption in the compressed domain to protect privacy, enabling direct analysis and watermark embedding on encrypted videos without decompression. A dual-channel watermarking strategy is designed to model and analyze CAVLC entropy coding. To enhance robustness, we select syntax elements with anti-recompression capabilities from the compressed domain for embedding and use a majority voting mechanism in the embedding algorithm. Additionally, we introduce an entropy coding-based watermark retrieval algorithm to improve detection accuracy through a sliding window approach. Experimental results show that our method reduces the re-compression error rate by over 44% compared to existing methods.

This study systematically uncovers and quantitatively evaluates the pervasive Orientalist biases in text-to-image (T2I) and text-to-video (T2V) generation models through a sociocultural lens grounded in postcolonial Orientalist theoretical frameworks. We identify systematic biases in the visual representations produced by multimodal generative models, including hyper-exoticization and temporal alienation. These biases mirror colonial-era narratives and undermine equitable sociocultural communication. Through empirical analysis of 8 mainstream T2I models and 4 T2V models, we demonstrate that culturally neutral prompts related to China consistently generate visual outputs embedded with Orientalist biases. We develop a novel visual question answering (VQA) framework as an evaluation metric, leveraging state-of-the-art vision-language model (VLM) to establish the first automated quantitative assessment methodology for such biases. A mitigation framework employing large language model (LLM) is proposed and experimentally validated. This interdisciplinary work illuminates the societal implications of multimodal generative models while advancing efforts toward fair and inclusive social computing.

In cloud computing, it is necessary to outsource image processing algorithms securely without exposing private image content. The scale-invariant feature transform (SIFT) is a famous local descriptor widely used in computer vision. There are already some privacy-preserving schemes for computing SIFT on encrypted images. However, the state-of-the-art works have to convert fixed-point numbers into their binary representations, which reduces efficiency and accuracy. In this paper, we propose a novel privacy-preserving SIFT scheme built from secure protocols designed explicitly for fixed-point numbers to solve this problem. Specifically, using RLWE-based homomorphic encryption, we propose word-wise protocols to perform secure division, square root operation, comparison, derivation, and matrix inversion in a single-instruction multiple-data manner. These protocols allow direct processing of fixed-point numbers without converting them to binary numbers, thus achieving high computational efficiency. We have also realized critical SIFT steps missing from previous works, including Euclidean gradient amplitude computation, histogram peak interpolation, and precise interval localization, leading to improved accuracy of SIFT features in the encrypted domain. We conduct security analysis and perform extensive experiments to evaluate the execution efficiency and accuracy. The experimental results show that the proposed scheme outperforms the state-of-the-art works in terms of computational efficiency and accuracy.

Privacy information existing in the scene text will be leaked with the spread of images in cyberspace. Vanishing the scene text from the image is a simple yet effective method to prevent privacy disclosure to the machine and the human. Previous visual text vanishing methods have achieved promising results but the performance still fell short of expectations for complicated-shape scene texts with various scales. In this paper, we propose a novel hierarchical context-aware interaction reconstruction method to make the visual text vanish in the natural scene image. To avoid the interference of the non-text regions, we narrow down the reconstruction regions by the guidance of the hierarchical refined text region masks, helping provide accurate position information. Meanwhile, we propose to learn the long-range context-aware interaction in a lightweight way, which can ensure the smoothing of the artifacts that are easily generated by the convolutional layers. To be more specific, we first simultaneously generate the coarse text region mask and the initially vanishing scene text image. Then, we obtain more accurate refined masks to better capture the locations of complicated-shape texts via a hierarchical mask generation network. Next, based on the refined masks, we exploit a channel-wise context-aware interaction mechanism to model the long-range relationships between the reconstruction region and the backgrounds for better removing the artifacts. Finally, we fuse the reconstructed text regions with the non-masked regions to obtain the ultimate protected image. Experiments on two frequently-used benchmarks SCUT-EnsText and SCUT-Syn demonstrate that our proposed method outperforms previous related methods by a large margin.

Online image super-resolution (SR) services have been widely used in applications such as Remini and DeepAI. However, the exposure of plaintext images raises serious privacy concerns. While secure CNN inference techniques are employed to protect images in image classification, they are not applicable to the unique challenges posed by image SR: the output resolution is significantly higher than that of the input image. In this paper, we present a secure CNN inference scheme for image SR by employing a multiple ciphertext encapsulation method. We begin by designing fundamental homomorphic operations, including addition, multiplication, and rotation across ciphertexts. Recognizing that image SR typically involves an upsampling layer-unlike image classification-we propose a fast algorithm for secure upsampling. This technique leverages pre-weight block masking and cross-ciphertext rotation, resulting in a significant speedup compared to direct homomorphic upsampling. We then present an efficient batched homomorphic two-dimensional convolution method across ciphertexts, incorporating kernel rearrangement and merging strategies. We also design a polynomial activation function specifically optimized for image SR, further enhancing performance. Extensive experiments demonstrate that our HE-friendly SR network outperforms existing secure solutions, while the proposed multiple ciphertext encapsulation technique achieves at least a 2x improvement in both computational efficiency and memory usage.

Homomorphic Encryption (HE) facilitates the preservation of privacy in federated learning (FL) aggregation. However, HE imposes significant computational and communication overhead. To address this problem, data encoding methods have been introduced that enable batch processing to improving the efficiency of ciphertext usage. The existing methods simply concatenate integer or coefficients assignment in polynomials, which do not fully make use of HE based on ring learning with errors (RLWE). We present a novel two-tier data encoding approach tailored for RLWE-based HE, effectively utilizing RLWE's polynomial structure. Our method involves a dual-level data packing strategy for batch processing at both integer and polynomial levels. At the first tier (integer level), we amalgamate those quantized model data into larger integers. Beyond existing concatenation-based encoding, we introduce a new encoding method derived from the Chinese Remainder Theorem (CRT). This CRT-based method effectively mitigates overflow and error propagation concerns. At the second tier (polynomial level), we transmute the large integers into a polynomial form. Additionally, we propose a new subring decomposition method, i.e., employing ring isomorphism mappings to project multiple large integers into varied sub-polynomial rings. Our dual-tier encoding strategy offers a more flexible and effective batch HE solution. We rigorously analyze the correctness, efficiency, and security of our approach. Our extensive experimental evaluations reveal that secure FL, empowered by our dual-tier encoding technique, markedly enhances computational and communication efficiencies over prevailing batch HE methods.

Deep learning solutions for privacy anonymization in video action recognition face significant computational challenges and difficulties in collecting privacy labels. To address these issues, we propose a fast privacy-preserving action recognition framework based on compressed video bitstreams. Our framework introduces two novel anonymization methods for video bitstreams, transforming video data directly within the compressed domain into anonymized compressed data to protect sensitive video information. The anonymized and compressed video data exhibits a compact data representation, enhancing transmission efficiency. We propose a technique to reconstruct anonymized compressed data in the spatial domain and utilize deep learning technology for high-accuracy action recognition. The experiment results demonstrate that our framework excels in balancing privacy protection performance with action recognition, showing a significant trade-off advantage. Our approach also achieves an operational efficiency more than fivefold greater than existing methods.

In the cloud, there is an urgent need to implement intelligent video surveillance in a privacy-preserving way. Moving object detection is an important task in the intelligent surveillance system. In this paper, we propose a privacy-preserving deep learning framework to detect moving objects on compressed videos. We encrypt video bitstreams using selective video encryption to protect the private video content. We propose encrypted domain motion information (EDMI) without decryption and decompression to design three motion feature maps. Due to the sparsity of the EDMI distribution, existing convolutional backbones designed for RGB images have difficulty providing satisfactory performance. We design a novel convolutional backbone using a ”subtraction” strategy to reduce model complexity. Our backbone employs residual blocks and skipping connections to reuse the EDMI at deeper layers. We evaluate our model on two large high-definition surveillance video datasets, i.e., VIRAT and Duke-MTMC. The experimental results show that the proposed framework achieves state-of-the-art detection performance compared with the most recent works. Our approach achieves an excellent privacy-utility tradeoff. Compared to previous solutions, it performs more robustly in crowded scenarios with challenges like occlusion. To our best knowledge, this is the first reported deep learning framework for moving object detection in encrypted-compressed video.

In comparison to symmetric embedding, asymmetric methods generally provide better steganography security. However, the performance of existing asymmetric methods is limited by their reliance on symmetric embedding costs. In this paper, we present a novel Generative Adversarial Network (GAN)-based steganography approach that independently learns asymmetric embedding costs from scratch. Our proposed framework features a generator with a dual-branch architecture and a discriminator that integrates multiple steganalytic networks. To address the issues of model instability and non-convergence that often arise in GAN model training, we implement an adaptive strategy that updates the GAN model parameters according to the performance of multiple steganalytic networks in each iteration. Furthermore, we introduce a new adversarial loss function that effectively learns asymmetric embedding costs by utilizing features like image residuals, gradients, asymmetric embedding probability maps, and the sign of the modification map to train the dual-branch network within the generator. Our comprehensive experiments show that our method achieves state-of-the-art steganography security results, significantly outperforming existing top-performing symmetric and asymmetric methods. Additionally, numerous ablation experiments confirm the rationality of our GAN-based model design.

In this paper, we propose an end-to-end keyword spotting scheme that applies deep learning techniques in a homomorphic encryption domain. Leveraging the complex number encryption capability of homomorphic encryption algorithms, we introduce a complex neural network and design every modules of it, to accommodate the properties and characteristics of homomorphic encryption. Considering the relatively slow computation speed of homomorphic encryption, we employ convolution decomposition to reduce the cost of computational of the network, effectively minimizing computational time while preserving network performance. We have tested our proposed method and found that it significantly accelerates computation speed with minimal performance sacrifice when compared to the current state-of-the-art methods, thereby better balancing network performance and efficiency.

Frequent itemset mining is a data mining technique widely used on massive datasets. In cloud computing, the dataset may be encrypted for privacy protection. Therefore, frequent itemset mining over encrypted data is a crucial application in secure cloud computing. In this paper, we propose an effective privacy-preserving framework where the cloud server can directly perform data mining on the encrypted database without interacting with other cloud servers. We first design three security primitives to implement subset determination, accumulation, and comparison in the encrypted domain for frequent itemset mining. Based on the proposed framework, we then propose two secure protocols that allow the cloud server to perform frequent itemset mining on encrypted cloud data with these security primitives. The first protocol leaks no information to the cloud and the second protocol has the advantage of more efficient mining performance. We then present two strategies with parallel algorithms and GPU computing to accelerate the running time. We also analyze the security of our protocols and the computational complexities. Experimental results show that our serial-based protocols achieve shorter running times and higher levels of privacy than previous solutions. Our multi-CPU (or GPU) based parallel protocol can further reduce the practical running time.

The recent literature has shown that adversarial embedding has promise for enhancing the security of steganography. However, existing methods achieve the final stego mainly based on a pre-trained Convolutional Neural Network (CNN)-based steganalyzer without considering any other steganalytic features. When the steganalyzer is re-trained, its performance usually drops significantly. We propose a novel adversarial embedding method via stego generation and selection. To improve the diversity of the stego images, this method first randomly generates many candidate stegos according to the amplitudes of the gradients and embedding costs of a given cover. Since the image residuals are the commonly used low-level features in many steganalyzers, the proposed method carefully designs different adaptive high-pass filters to calculate the image residuals, and then selects a final stego from among those candidate stegos which can successfully fool the pre-trained steganalyzer, according to the residual distance between stego and the cover. Extensive experimental evaluations on re-trained CNN-based and traditional steganalyzers demonstrate that the proposed method can significantly enhance the security of the modern steganographic methods in both spatial and JPEG domains, and achieve much better performance than related adversarial embedding methods.

In this paper, we propose a non-interactive scheme to achieve end-to-end keyword spotting in the homomorphic encrypted domain using deep learning techniques. We carefully designed a complex-valued convolutional neural network (CNN) structure for the encrypted domain keyword spotting to take full advantage of the limited multiplicative depth. At the same depth, the proposed complex-valued CNN can learn more speech representations than the real-valued CNN, thus achieving higher accuracy in keyword spotting. The complex activation function of the complex-valued CNN is non-arithmetic and cannot be supported by homomorphic encryption. To implement the complex activation function in the encrypted domain without interaction, we design methods to approximate complex activation functions with low-degree polynomials while preserving the keyword spotting performance. Our scheme supports single-instruction multiple-data (SIMD), which reduces the total size of ciphertexts and improves computational efficiency. We conducted extensive experiments to investigate our performance with various metrics, such as accuracy, robustness, and F1-score. The experimental results show that our approach significantly outperforms the state-of-the-art solutions on every metric.

As individuals and companies become more aware of copyright, image watermarking technology is being used more and more in reality. Due to the limitation of local storage and computing power and privacy protection concerns, privacy-preserving watermarking techniques based on cloud computing are gradually gaining more and more attention. We propose an encrypted image watermarking scheme based on discrete cosine transform (DCT) under cloud computing. Using homomorphic encryption (HE), we achieve secure embedding of watermarks on images in the homomorphic encryption domain. The cloud server accomplishes the watermark embedding algorithm. During the embedding process, the cloud server does not need to decrypt the encrypted image, thus ensuring the security of the original image data. We theoretically analyze the security and computational complexity of the whole scheme. Our experimental results show the effectiveness and reliability of our watermarking scheme.

People encounter a variety of continuous decision-making (DM) problems in the real world. Reinforcement learning (RL) is a promising technique to solve these problems. This paper proposes a privacy-preserving Q-learning decision-making scheme (PQDM). Based on distributed homomorphic encryption (HE), we design several secure protocols to implement the under-lying nonlinear operations such as comparing, maximizing, and maximizing parameter solving. Based on the designed security protocols, we propose a secure decision-making protocol in cloud computing, which enables the cloud server to perform element selection and Q-learning functions on ciphertext data. During the entire process, the cloud server does not need to know the actual state, thus guaranteeing the security of the original state information. We analyze the security and complexity of the whole scheme theoretically. Our experimental results show our proposed scheme’s effectiveness and good spatio-temporal performance.

Unlike most existing steganography methods which are mainly focused on designing embedding cost, in this paper, we propose a new method to enhance existing steganographic methods via stego generation and selection. The proposed method firstly trains a steganalytic network according to the steganography to be enhanced, and then tries to adjust a tiny part of original embedding costs based on the magnitudes of it and the corresponding gradients obtained from the pre-trained network, and generates many candidate stegos in a random manner. Finally, the method selects a stego according to its image residual distance to cover. Extensive experimental results have shown that the proposed method can siginficantly enhance the security performance of current steganography in spatial domain against four steganalytic classifiers. In addition, comparative analysis between original stegos and the resulting ones with the proposed method are given.

Signal Processing in the Encrypted Domain (SPED) has received considerable attention as it aims at privacy-preserving solutions for various applications. Discrete cosine transform (DCT) is a popular signal transform widely used in signal processing. It has many applications in speech processing, still picture coding, image and video transformation, compression coding, and so on. In this paper, we mainly study how to implement DCT in the encryption domain with high precision. We propose a new scheme to implement encrypted domain DCT. This scheme encodes a complex number as a unit root polynomial in the evaluation, and realizes the high precision representation of complex numbers. With this representation, this scheme can also realize the high precision representation of decimals. To improve the computational efficiency, we also propose a fast implementation of DCT in the encryption domain, which can significantly improve the DCT speed for large-scale matrices. We conducted experiments to verify the effectiveness and efficiency. When the matrix size is small, our original method has the advantages of both high accuracy and fast speed. When the matrix size is large, the fast implementation outperforms the original method.

Line detection is an important research topic in image processing and computer vision. Hough transform is a widely used technique to detect lines. In the scenario of cloud computing, performing the Hough transform and line detection needs to consider privacy protection issues. In this paper, we propose implementing Hough transform in the encrypted domain and its application to line detection on encrypted images. Using the proposed encrypted domain Hough transform, we detect the extreme points in the encrypted parameter space. After transforming the extreme point into the encrypted spatial domain, we obtain the detected lines on the encrypted image. We conducted experiments and demonstrated the viability and effectiveness of our secure Hough transform and line detection on encrypted images.

Image steganography aims to embed secret messages into cover images in an imperceptible manner. While steganalysis tries to identify stegos from covers, which is a special binary classification problem. Recently, some literatures show that the adversarial embedding can mislead the advanced steganalyzers based on convolutional neural network (CNN), and thus enhance the steganography security. Since adding perturbations to stegos may lead to messages extraction failure due to properties of syndrome-trellis codes (STC), the existing adversarial examples are derived from covers or their enhanced versions, while those stegos are not fully utilized. In this paper, we propose a new adversarial embedding scheme for image steganography. Unlike those related works, we first combine multiple gradients of cover and generated stegos to determine the directions of cost modifications. Next, instead of adjusting all or a random part of embedding costs in existing works, we carefully select the candidate costs according to the amplitudes of cover gradients and their costs. Extensive experimental results demonstrate that by adjusting a tiny part of embedding costs (less than 5% in most cases), the proposed method can significantly improve the security of five modern steganographic methods evaluated on both re-trained CNN-based and traditional steganalyzers, and achieve much better security performances compared with related methods. In addition, the security performances evaluated on different image database show that the generalization of the proposed method is good.

Video privacy leakage is becoming an increasingly severe public problem, especially in cloud-based video surveillance systems. It leads to the new need for secure cloud-based video applications, where the video is encrypted for privacy protection. Despite some methods that have been proposed for encrypted video moving object detection and tracking, none has robust performance against complex and dynamic scenes. In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams. By analyzing the properties of the video codec and format-compliant encryption schemes, we propose a new compressed-domain feature to capture motion information in complex surveillance scenarios. Based on this feature, we design an adaptive clustering algorithm for moving object segmentation with an accuracy of 4×4 pixels. We then propose a multiple object tracking scheme that uses Kalman filter estimation and adaptive measurement refinement. The proposed scheme does not require video decryption or full decompression and has a very low computation load. The experimental results demonstrate that our scheme achieves the best detection and tracking performance compared with existing works in the encrypted and compressed domain. Our scheme can be effectively used in complex surveillance scenarios with different challenges, such as camera movement/jitter, dynamic background, and shadows.

In cloud computing, videos may be in an encrypted format to protect privacy. Therefore, encrypted video processing is an important application in secure cloud computing. In this paper, we focus on parameter estimation and anomaly detection in an encrypted video bitstream. By analyzing the common properties of video encoding frameworks and the format-compliant encryption schemes, we propose an anomaly detection scheme for encrypted video bitstream with format-compliant encryption. From the encrypted bitstream, we extract three types of complementary features, i.e., the macroblock sizes, the macroblock partitions, and the motion vector difference magnitude, and then propose a method to combine these three features. The proposed detection and localization scheme does not involve video decryption, full decompression, or an interactive protocol, which makes it efficient. Our scheme is also compatible with different video encryption methods. To accelerate the running time, we develop a parallel implementation for our scheme. The experimental results show that our method achieves good running time and detection rate performance.

Most modern steganography methods focus on designing an effective cost function. To our best knowledge, there is no related works concerned about modifying stego to enhance steganography security. In this paper, therefore, we propose a novel post-processing for stego image in the spatial domain. To ensure the correct extraction of hidden message, our method restricts the modification amplitude of each pixel according to the characteristics of STCs (Syndrome-Trellis Codes). To enhance steganography security, our method traverses the stego image pixel by pixel, and modifies those pixels that can reduce the image residual difference between cover and stego under some criterion. Experimental results show that the proposed method can improve the security of current steganography especially for large payloads, e.g. larger than 0.3 bpp. In addition, the post-modification rate is rather low, for instance less than 8 \textpertenthousand \ pixels have been changed in the enhanced stego image for the five existing steganography methods for payload as large as 0.5 bpp.

Edge detection is one of the most important topics of image processing. In the scenario of cloud computing, performing edge detection may also consider privacy protection. In this paper, we propose an edge detection and image segmentation scheme on an encrypted image with Sobel edge detector. We implement Gaussian filtering and Sobel operator on the image in the encrypted domain with homomorphic property. By implementing an adaptive threshold decision algorithm in the encrypted domain, we obtain a threshold determined by the image distribution. With the technique of garbled circuit, we perform comparison in the encrypted domain and obtain the edge of the image without decrypting the image in advanced. We then propose an image segmentation scheme on the encrypted image based on the detected edges. Our experiments demonstrate the viability and effectiveness of the proposed encrypted image edge detection and segmentation.

Since homomorphic encryption operations have high computational complexity, image applications based on homomorphic encryption are often time consuming, which makes them impractical. In this paper, we study efficient encrypted image applications with the encrypted domain Walsh-Hadamard transform (WHT) and parallel algorithms. We first present methods to implement real and complex WHTs in the encrypted domain. We then propose a parallel algorithm to improve the computational efficiency of the encrypted domain WHT. To compare the WHT with the discrete cosine transform (DCT), integer DCT, and Haar transform in the encrypted domain, we conduct theoretical analysis and experimental verification, which reveal that the encrypted domain WHT has the advantages of lower computational complexity and a shorter running time. Our analysis shows that the encrypted WHT can accommodate plaintext data of larger values. We propose two encrypted image applications using the encrypted domain WHT. To accelerate the practical execution, we present two parallelization strategies for the proposed applications. The experimental results show that the speedup of the homomorphic encrypted image application exceeds 12.

Performing detection on surveillance videos contributes significantly to the goals of safety and security. However, performing detection on unprotected surveillance video may reveal the privacy of innocent people in the video. Therefore, striking a proper balance between maintaining personal privacy while enhancing the feasibility of detection is an important issue. One promising solution to this problem is to encrypt the surveillance videos and perform detection on the encrypted videos. Most existing encrypted signal processing methods focus on still images or small data volumes; however, because videos are typically much larger, investigating how to process encrypted videos is a significant challenge. In this article, we propose an efficient motion detection and tracking scheme for encrypted H.264/AVC video bitstreams, which does not require the previous decryption on the encrypted video. The main idea is to first estimate motion information from the bitstream structure and codeword length and, then, propose a region update (RU) algorithm to deal with the loss and error drifting of motion caused by the video encryption. The RU algorithm is designed based on the prior knowledge that the object motion in the video is continuous in space and time. Compared to the existing scheme, which is based on video encryption that occurs at the pixel level, the proposed scheme has the advantages of requiring only a small storage of the encrypted video and has a low computational cost for both encryption and detection. Experimental results show that our scheme performs better regarding detection accuracy and execution speed. Moreover, the proposed scheme can work with more than one format-compliant video encryption method, provided that the positions of the macroblocks can be extracted from the encrypted video bitstream. Due to the coupling of video stream encryption and detection algorithms, our scheme can be directly connected to the video stream output (e.g., surveillance cameras) without requiring any camera modifications.

The field of image processing in the encrypted domain has been given increasing attention for the extensive potential applications, for example, providing efficient and secure solutions for privacy-preserving applications in untrusted environment. One obstacle to the widespread use of these techniques is the ciphertext expansion of high orders of magnitude caused by the existing homomorphic encryptions. In this paper, we provide a way to tackle this issue for image processing in the encrypted domain. By using characteristics of image format, we develop an image encryption scheme to limit ciphertext expansion while preserving the homomorphic property. The proposed encryption scheme first encrypts image pixels with an existing probabilistic homomorphic cryptosystem, and then compresses the whole encrypted image in order to save storage space. Our scheme has a much smaller ciphertext expansion factor compared with the element-wise encryption scheme, while preserving the homomorphic property. It is not necessary to require additional interactive protocols when applying secure signal processing tools to the compressed encrypted image. We present a fast algorithm for the encryption and the compression of the proposed image encryption scheme, which speeds up the computation and makes our scheme much more efficient. The analysis on the security, ciphertext expansion ratio, and computational complexity are also conducted. Our experiments demonstrate the validity of the proposed algorithms. The proposed scheme is suitable to be employed as an image encryption method for the applications in secure image processing.

Signal processing in the encrypted domain is a new technology with the goal of protecting valuable signals from insecure signal processing. In this paper, we propose a method for implementing discrete wavelet transform (DWT) and multiresolution analysis (MRA) in homomorphic encrypted domain. We first suggest a framework for performing DWT and inverse DWT (IDWT) in the encrypted domain, then conduct an analysis of data expansion and quantization errors under the framework. To solve the problem of data expansion, which may be very important in practical applications, we present a method for reducing data expansion in the case that both DWT and IDWT are performed. With the proposed method, multilevel DWT/IDWT can be performed with less data expansion in homomorphic encrypted domain. We propose a new signal processing procedure, where the multiplicative inverse method is employed as the last step to limit the data expansion. Taking a 2-D Haar wavelet transform as an example, we conduct a few experiments to demonstrate the advantages of our method in secure image processing. We also provide computational complexity analyses and comparisons. To the best of our knowledge, there has been no report on the implementation of DWT and MRA in the encrypted domain.

Signal processing in the encrypted domain is a new technology for protecting valuable signals from insecure signal processing. Although there has been some research in the area, this field of research is still in its infancy.