To address the limitations of current 3D mesh watermarking in robustness and imperceptibility, this paper proposes a deep watermarking based on a geometric-weighted aggregation mechanism. The message encoder and decoder networks are first improved to enable the effective embedding of 16-bit binary watermark information. An attack simulation module is then introduced to enhance the decoder's robustness against various distortions. Additionally, an adversarial discriminator is incorporated to guide the encoder in optimizing the embedding strategy, thereby minimizing geometric distortion. Furthermore, a cross-resolution strategy is developed to enable training on low-resolution meshes and perform watermark embedding and extraction on high-resolution meshes. Experimental results demonstrate that it outperforms the existing mainstream approaches in terms of extraction accuracy, geometric fidelity, and imperceptibility.

In recent years, video steganography technique based on H.265/HEVC has received widespread attention. Typically, video steganography selects various syntax elements during the encoding process as carriers, and utilizing Prediction Unit (PU) as carrier is currently one of the most significant research directions. However, due to the limited number of PU types, such algorithms often suffer from insufficient capacity and visual quality. To alleviate the aforementioned issues, this paper proposes an H.265/HEVC video steganography algorithm that utilizes polygon encoding and Improved Deep Learnable Similarity Network Filter (IDLSNF). Firstly, we design a new polygon encoding rule, which maps different integers into several polygons. Secondly, we propose a novel steganography method based on polygon encoding and PU partition mode. This method selects the PUs of$8\times 8$and$16\times 16$coding unit in P-frames as carriers and hides the secret message by modifying the partition mode of two adjacent PUs. Due to the ability of polygon encoding to represent more information within a small range, it increases capacity with low steganographic distortion. Thirdly, we further propose a filter by improving DLSN, which enhances the visual quality of the entire stego video by processing I-frames. Extensive experimental results show that the video steganography algorithm proposed in this paper achieves higher capacity and superior visual quality compared to current State-of-the-Art methods. Meanwhile, our algorithm can also obtain good BRI and anti-steganalysis performance. This method has promising application prospects in the field of video covert communication.

Existing adversarial attacks for face anti-spoofing predominantly assume that the model parameters are fully known, and often overlook the transferability of adversarial examples across different models and domains. Furthermore, they typically target relatively homogeneous architectures, relying primarily on basic deep learning models for anti-spoofing. To address these limitations, this paper proposes an illumination-based input transformation method for generating adversarial attacks. A liveness ablation module is introduced to suppress liveness-related cues in the input image prior to attack generation, thereby enhancing the adversarial strength of the crafted examples. Additionally, a random illumination transformation strategy is employed to increase domain divergence by altering illumination factors, which enriches the diversity of input samples and boosts the transferability of adversarial examples across different models and settings. Extensive experiments conducted on two public datasets demonstrate that the proposed method outperforms existing approaches in terms of physical-world transferability. Moreover, the liveness ablation module can be integrated with other attack strategies to furture improve their adversarial effectiveness.

With the rapid development of the metaverse, massive amounts of 3D data are created and outsourced in the cloud, and ciphertext policy attribute-based encryption (CP-ABE) is widely used in fine-grained access control to achieve secure outsourced data sharing. However, the prominent security risk is due to the fact that authorized data users may later become traitors and illegally redistribute the 3D models to the public. To protect the rights of the creator, a traitor tracing and access control method for encrypted 3D models is proposed using CP-ABE and fair watermark to meet the security needs in the metaverse. First, a commutative watermark/encryption method based on the orthogonal operation domain is designed, and the 3D model is encrypted by CP-ABE. Then, a fair watermark protocol protects the rights of the parties. Finally, the blockchain acts as a trusted third party and records the authentication information for traitor tracing. The experimental results demonstrate the feasibility and safety of the proposed method.

With the rapid development of Deepfake technology, social security is facing great challenges. Although numerous Deepfake detection algorithms based on traditional CNN frameworks perform well on specific datasets, they still suffer from overfitting due to an over-reliance on localized artifact information. This limitation leads to degraded detection performance across diverse datasets. To address this issue, this study proposes a dual-branch fusion network called LGDF-Net. LGDF-Net uses a dual-branch structure to process the local artifact features and global texture features generated by Deepfake separately, preserving their unique characteristics. Specifically, the local compression branch utilizes a specially designed local compression module (LCM) that allows the network to focus more accurately on key regions of localized artifacts in Deepfake faces. The global expansion branch enhances the analysis of the global facial context through a global expansion module (GEM), which captures image context information and subtle texture features more comprehensively. Additionally, the proposed multi-scale feature extraction module (MSFE) delves into image features at various scales, enriching the extraction of detailed information. Finally, the multi-level feature fusion strategy (MLFF) improves the integration of local and global features through multiple layers, enabling the network to learn the intrinsic connections between these two types of features. A series of experimental validations demonstrate that the proposed scheme outperforms many existing detection networks in terms of accuracy and generalization ability.

Most face anti-spoofing methods address the generalization problem by extracting domain-invariant representations from multiple source domains or unlabelled target data. However, their deployment in real-world applications is unfeasible when data is insufficient or unavailable due to the collection costs and privacy concerns. This work investigates a more practical yet challenging scenario: single-source domain generalization based face anti-spoofing, where only one source domain is available during training and evaluated on multiple unseen target domains. To tackle this problem, a Causality-inspired Single-source Domain Generalization method (CSDG) is developed, which focuses on learning causal spoofing representations from the causality perspective. Specifically, a causal diagram is constructed to estimate the fundamental properties of ideal causal spoofing representations: remain invariant to shifts of domain-related confounders and causally sufficient for the detection category. To satisfy the above properties, the Causal Learning Module (CLM) maximizes the correlation of representations before and after intervention and minimizes the correlation with negative distributions. The intervention is achieved by arbitrarily performing spectrum mixup and structure destruction on source data within the Causal Intervention Model (CIM). Extensive experiments on four benchmark datasets validate the effectiveness of the proposed method.

Aiming at reducing the embedding distortion and improving tamper localization accuracy, a semi-fragile reversible watermarking for 3D models based on IQIM (improved quantization index modulation) with dual-strategy partition modulation is proposed. Firstly, watermark is built based on the position data of vertices and the number of neighboring vertices within the first ring. With IQIM, a dual-strategy partition based on the relationship between the position of quantization coefficients within quantization units and the watermark information is built. It performs single or dual modulation of IQIM to vertices, which is helpful for reducing the distortion induced by watermark embedding in the 3D models. During watermark extraction and data recovery, the watermark is first extracted based on the model vertex information and the original watermark is regenerated. The dual-strategy partition is performed based on the relationship between the position of quantization coefficient within quantization units and the regenerated watermark. Then, the model vertices are respectively recovered. Finally, by comparing the consistency between the extracted and regenerated watermarks, the integrity of the 3D model can be authenticated and the tampered area can be located. The experiments and analysis show that it outperforms several cutting-edge techniques from the aspects of embedding distortion and tamper localization capability.

Abstract Morphing attacks (MAs) pose a substantial security threat to the Automatic Border Control (ABC) system. While a few morphing attack detection (MAD) methods have been proposed, the face morphing accomplice's facial restoration has not received sufficient attention. Due to the inability to foresee the morphing factor used for a particular morphed image, selecting the appropriate de‐morphing factor becomes a challenging problem in the restoration of the accomplice's facial image. If the morphing factor cannot be chosen reasonably, achieving the desired restoration effect is difficult. Therefore, this paper presents an adaptive de‐morphing factor framework (ADFF) architecture for restoring the accomplice's facial image. By exploiting the morphed images stored in the electronic passport system and the real‐time captured criminal's images, ADFF can effectively restore the accomplice's facial image. Experimental results and analysis show that ADFF can significantly reduce the security threats of MAs on ABC.

Aiming at the shortcomings of the current face liveness detection attack methods in the low generation speed of adversarial examples and the implementation of white‐box attacks, a novel black‐box attack method for face liveness detection named as FLDATN is proposed based on adversarial transformation network (ATN). In FLDATN, a convolutional block attention module (CBAM) is used to improve the generalization ability of adversarial examples, and the misclassification loss function based on feature similarity is defined. Experiments and analysis on the Oulu‐NPU dataset show that the adversarial examples generated by the FLDATN have a good black‐box attack effect on the task of face liveness detection and can achieve better generalization performance than the traditional methods. In addition, since FLDATN does not need to perform multiple gradient calculations for each image, it can significantly improve the generation speed of the adversarial examples.

In view of inconsistent face acquisition procedure in face anti-spoofing, the detection performance on the target domain generally suffers severe degradation under source-specific gradient optimization. Existing domain adaptation face anti-spoofing methods focus on improving model generalization capability through feature matching, which do not consider the gradient discrepancy between the source and target domains. To this end, this work develops a category-conditional gradient alignment guided face anti-spoofing algorithm (CCGA-FAS) from a novel perspective of gradient discrepancy elimination. Technically, the category-conditional gradient alignment mechanism maximizes the cosine similarity of the gradient vectors generated by source and target samples within the live and spoof categories separately, which promotes the source and target domains to follow similar gradient descent directions during optimization. Considering that the gradient vector generation and alignment is computationally dependent on reliable category information, a temporal knowledge and flexible threshold based dynamic category measurer is devised to provide pseudo category information for unlabelled target samples in an easy-to-hard manner. The optimization for CCGA-FAS is implemented under the teacher-student structure, where the student model serves as the gradient optimization backbone, and the category prediction simultaneously benefits from the teacher and student models to consolidate the alignment stability. Experimental results and analysis demonstrate that the proposed method outperforms the state-of-the-art methods in both unsupervised and K-shot semi-supervised domain adaptive face anti-spoofing scenarios.

Face morphing attacks pose a significant threat to society as they disrupt the one-to-one mapping between facial images and identity features in face recognition systems. Despite the development of several detection methods to counter such attacks, the task of restoring the facial image of the accomplice from the morphed facial image, known as face de-morphing, remains a challenging problem. In this paper, we propose a novel diffusion-based method for face de-morphing. This method employs pre-trained diffusion autoencoders to encode the image into two subspaces: a semantic latent space that captures identity features and a stochastic latent space that retains the remaining stochastic details. To ensure the effective separation of identity features, a dual-branch identity separation network is constructed in the semantic latent space. This network utilizes a cross-attention inverse linear interpolation branch to separate the accomplice’s semantic latent code and a multilayer perceptron branch to complement the separated latent code. Additionally, the morphed stochastic latent code is empirically chosen as the accomplice’s stochastic latent code. Finally, a conditional denoising diffusion implicit model is used to decode the latent code of the two subspaces, thus achieving the restoration of the accomplice’s facial image. Experimental results and analysis demonstrate that the proposed method outperforms existing face de-morphing methods in terms of restoration accuracy and image quality.

Reversible data hiding in encrypted domain (RDH-ED) can perform data encryption to fulfill the privacy protection of original media and embed additional data for covert communication or access control. However, current researches are focusing on the encrypted images, and little attention is paid to encrypted three-dimensional (3D) models. In this article, a high capacity separable RDH-ED method for encrypted 3D models is proposed based on octree spatial subdivision and multiple most significant bit (multi-MSB) prediction. Firstly, a 3D model is adaptively subdivided into non-overlapping subblocks by octree spatial subdivision, and the vertices in a subblock are classified into embedding set and reference set. To better utilize the spatial correlation of the two sets, the multi-MSB prediction error of the embedding set is used to embed the additional data, and the reference set is used to losslessly recover the embedded set. Then, the model is encrypted by a specified encrypted algorithm. At last, additional data is embedded into the reserved embedding room by multi-MSB substitution. Experimental results show that the proposed method can achieve a higher embedding capacity compared with the state-of-the-art methods, and guarantee the lossless recovery of the 3D model.

To counter adversarial facial accessory presentation attacks (PAs), a detection method based on local face differential is proposed in this article. It extracts the local face differential features from a suspected face image and a reference face image, and then adaptively fuses the differential features of different local face regions to detect adversarial facial accessory PAs. Meanwhile, the principle of the proposed method is explained by theoretically investigating the local facial differences between a bona fide presentation and an adversarial facial accessory PA when they are compared with a reference face image. To evaluate the proposed method, this article builds a database with different adversarial examples (AEs), presentation attack instruments (PAIs), illumination conditions, and cameras. The experimental results show that it can effectively distinguish between adversarial facial accessory PAs and bona fide presentations, and it has good generalization ability to unseen AEs, PAIs, illumination conditions, and cameras. Moreover, it outperforms the existing AE detection and presentation attack detection methods in detecting adversarial facial accessory PAs.

Aiming at the large distortion and low tampering localization accuracy of the existing semi-fragile reversible watermarking for 3D mesh models, a novel semi-fragile reversible watermarking for 3D models using spherical crown volume division is proposed. The crown volume of a sphere is divided to reduce the embedding distortion. The possible geometric and topological transformations are separately considered in the watermark generation, and the vertices of the one-ring neighbourhood are grouped to improve the tampering localization accuracy. Experimental results show that the proposed scheme can achieve better localization accuracy and lower embedding distortion than some state-of-the-art algorithms. It has good potential for the applications in integrity authentication for 3D mesh models.

To resolve the distortion resulting from the mismatch between the interval partition and the watermark distribution in the reversible watermarking based on difference expansion, an investigation is made to difference expansion with adaptive interval partitioning. It is found that the conventional floating number-based methods with uniform interval partitioning are not fully considering different watermark distributions, and the embedding distortion can be optimized. Thus, a reversible watermarking based on difference expansion with adaptive interval partitioning is put forward for 2D engineering graphics. Each interval is adaptively partitioned into$2^{s}$sub-intervals, and the watermark is embedded via transforming the positions of the vertices to its corresponding sub-intervals. The watermark extraction is accomplished by acquiring the indices of the sub-intervals of the watermarked vertices, and the graphics recovery is achieved by moving the watermarked vertices to the reverse direction. Moreover, a new polar coordinate system is built to be invariant to overall geometric transformation and vertices rearrangement. Experimental results and analysis indicate that it maintains good authentication ability and semi-fragility. Furthermore, its imperceptibility is significantly improved compared with the existing watermarking schemes under the same experimental conditions.

Aiming to reduce the embedding distortion and improve tampering location precision of reversible watermarking for authenticating three-dimensional(3D) models, a semi-fragile reversible watermarking based on virtual polygon projection and double modulation strategy is proposed. During the embedding, it first constructs virtual adjacent vertices for each vertex and obtains a corresponding virtual polygon, and then a watermark is generated according to the projection value of the current vertex on the corresponding polygon. For each vertex, double modulation is used to move the vertex to realize watermark embedding. For the verification, it first obtains the vertex position and extracts the watermark, and then regenerates a watermark according to the restored vertex. If the extracted watermark is consistent with the regenerated one, it means that the vertex has not been tampered, and the 3D model can be lossless recovered; otherwise, the vertex is tampered. Experimental results and analysis show that the proposed scheme outperforms the existing methods in embedding distortion and tampering location precision. It has potential application in the integrity authentication of 3D models.

To reduce the distortion resulting from the large number of crossing quantization cells and resist steganalysis, a reversible data hiding scheme for 2D engineering graphics is put forward based on reversible dual-direction quantization index modulation (RDQIM). The quantization cell index of the host data is first computed, and its distances to the embedding cells in both the left and the right directions are calculated. After that, the data hiding is performed by modifying the data to the nearest embedding cell. To guarantee the reversibility, each quantization cell is further subdivided into three sub-cells, and the source quantization interval of the host data is marked by the index of the located sub-cell. The data extraction is accomplished by calculating the index of the quantization cell where the stego data is in. Meanwhile, the lossless recovery of the stego data is realized by combining the index of the located sub-cell and the relative distance within the sub-cell. Besides, different embedding strategies are adopted for different types of entities to achieve steganalysis-resistant ability. Experimental results and analysis show that the proposed scheme can strike a good balance among imperceptibility, semi-fragility, and steganalysis-resistant ability. Moreover, under the same conditions, the average imperceptibility and the average capacity are, respectively, improved by at least 7.487% and 41.045% compared with the existing methods.

Aiming at resolving the problem of the irreversibility in some common neural networks for secret data extraction, a novel image steganography framework is proposed based on the generator of GAN (Generative Adversarial Networks) and gradient descent approximation. During data embedding, the secret data is first mapped into a stego noise vector by a specific mapping rule, and it is input into the generator of a GAN to produce a stego image. The data extraction is accomplished by iteratively updating the noise vector using the gradient descent with the generator. When the error is declined within the allowable error, the output image of the generator is approximate to the stego image, and the updated noise vector will also approach to the stego noise vector. Finally, the secret data is extracted from the updated noise vector. Experiments and analysis with WGAN-GP (Wasserstein GAN-Gradient Penalty) show that it can achieve good performance in extraction accuracy, capacity and robustness. Furthermore, the discussions also illustrate its good generalization with different GAN models and image datasets.

Aiming at reducing the large distortion of the existing reversible watermarking for three-dimensional(3D) mesh models and meeting the needs of authentication in cloud-based data storage, a semi-fragile reversible watermarking for authenticating 3D models in dual domains is proposed based on variable direction double modulation. A 3D model is first transformed into the spherical coordinate system, and then the direction of quantization modulation is changed under specific circumstances to achieve less distortion. Watermarks are determined by the relative position and vertices number in the one-ring neighbourhood of all vertices, so the watermark generation is independent of the coordinate system and robust to RST(rotation, scaling, and translation). In addition, the watermarks are generated and embedded in both plaintext domain and encrypted domain, and the authentication can be accomplished in both domains. Experimental results and analysis show that the proposed scheme can effectively detect malicious tampering in two domains and reduce the distortion caused by watermark embedding. It has potential application in the content authentication of 3D models in cloud-based data storage.

Aiming at degrading the capability of the existing forensic methods in discriminating computer generated and natural facial images, a bidirectional conversion between computer-generated and natural facial images based on generative adversarial network (BDC-GAN) is proposed for anti-forensics in this paper. The generator of BDC-GAN is composed of noise encoding and content encoding. In the noise encoding, three high-pass filters are first utilized to extract the sensor pattern noise of the image, and then the stacked convolution layer is combined to continue encoding. In the content encoding, VGG-19 is truncated and fine-tuned to encode the content of the image. Some stacked convolution layers and adaptive instance normalization layer are used in the decoder. The discriminator uses multi-scale image discriminator. Furthermore, content loss and noise loss are well designed, and hyperparameters are reasonably set to accomplish the bidirectional conversion between two domain images meanwhile retaining the original facial contour. Experimental results and analysis demonstrate that the proposed anti-forensic method can achieve better visual quality and stronger deception ability compared with the existing unidirectional CG facial image anti-forensic methods and bidirectional domain adaptive methods, and its effectiveness is verified by the tests on the existing 9 forensic methods. It reveals that the existing forensic techniques can be bypassed by using adversarial learning, and it will eventually push the performance improvement of the discrimination of computer generated and natural facial images.

To strengthen the security of face recognition systems to morphing attacks (MAs), many countermeasures were proposed. However, in the existing face morphing attack detection (MAD), the deep networks trained by classical score-level losses are weak in characterizing the intrinsic morphing patterns of different MAs, and they also cannot be directly applied to differential MAD scenarios. To this end, this paper presents a method for detecting and locating face MAs by the use of feature-wise supervision. It constructs the fine-grained classification loss on the basis of different morphing patterns, and designs the similarity-based and distance-based differential losses according to the properties of differential MAD scenarios. The experimental results and analysis show that the fine-grained classification loss can locate the local morphed areas after detecting MAs, while the differential losses are able to improve the generalization ability of MAD methods to unseen MAs, and can enhance the robustness of MAD methods to low-resolution and non-frontal probe face images.

As face presentation attacks (PAs) are realistic threats for unattended face verification systems, face presentation attack detection (PAD) has been intensively investigated in past years, and the recent advances in face PAD have significantly reduced the success rate of such attacks. In this article, an empirical study on a novel and effective face impostor PA is made. In the proposed PA, a facial artifact is created by using the most vulnerable facial components, which are optimally selected based on the vulnerability analysis of different facial components to impostor PAs. An attacker can launch a face PA by presenting a facial artifact on his or her own real face. With a collected PA database containing various types of artifacts and presentation attack instruments (PAIs), the experimental results and analysis show that the proposed PA poses a more serious threat to face verification and PAD systems compared with the print, replay, and mask PAs. Moreover, the generalization ability of the proposed PA and the vulnerability analysis with regard to commercial systems are also investigated by evaluating unknown face verification and real-world PAD systems. It provides a new paradigm for the study of face PAs.

Aiming to reduce the distortion in the existing reversible watermarking for 3D mesh models, this paper generalizes two-dimensional (2D) region nesting to${n}$-dimensional space, and proposes a general region nesting based semi-fragile reversible watermarking for authenticating 3D mesh models by combing watermark generation based on vertex projection and mesh topology. For any${n}$-dimensional space, each hypercube space is partitioned into several nested watermark sub-spaces, and the watermark is embedded by placing the original vertex and the mapped vertex on a straight line. The watermark is extracted by acquiring the position of the vertices, and its original coordinates can be restored by inverse mapping. Furthermore, a new coordinate system is built for 3D mesh models based on reference vertices to achieve semi-fragility, and the watermarks generated by vertex projection and mesh topology is utilized for integrity authentication. Experimental results and analysis show that the proposed scheme can significantly reduce the distortion, achieve semi-fragility in RST (Rotation, Scaling and Translation) and vertex reordering, identify the tampering type, and accomplish tampering location at vertex level. It has great potential to be applied for content authentication of 3D mesh models.

To achieve high tampering localization precision and low distortion, a semi-fragile reversible watermarking for authenticating 2D engineering graphics is proposed based on an improved region nesting and a novel watermark generation. First, an intensive investigation is performed to the recently proposed region nesting (RN) partition. It is found that the original vertex and its mapped one cannot be guaranteed to be on the same line, which means that it still has room for improvement in term of distortion. Based on this, an improved region nesting partition (IRN) is proposed. Secondly, inspiring by the idea of soldiers parading, a novel watermark generation based on the short hash of the adjacent geometric features is developed. Then, a new coordinate system is constructed to achieve invariability of translation, scaling, rotation and entity re-arrangement. Based on the above techniques, a semi-fragile reversible watermarking for authenticating 2D engineering graphics is put forward. Experimental results and analysis show that IRN can obtain at least 15% distortion reduction compared with RN, the precision of tampering localization can be improved to a single vertex, and a certain semi-fragility in translation, scaling, rotation, and entity re-arrangement can be achieved. Furthermore, it has no file size expansion, and can obtain a good balance among imperceptibility, semi-fragility and tampering localization precision.

Aiming to countermeasure facial privacy disclosure of the shared images in social media, a face privacy protection framework based on component difference and template morphing is proposed. For a shared facial image that requires privacy protection, its facial attributes are first detected, and then the most suitable face template is searched from a pre-built facial image template library. After that, the key points of the facial image and the face template are detected, and they are implemented for facial components segmentation. Finally, the facial components of two images are morphed according to the privacy protection level and the optimal morphing sequence determined by the component difference. Experiments and analysis are performed to an implementation of the framework. The results show that it can effectively protect the facial privacy meanwhile keep the visual quality of the image. It has great potential to be applied for privacy protection of the shared facial images in social media.

To accomplish robust watermark extraction in reversible watermarking both in plaintext domain and encrypted domain, a separable robust reversible watermarking in encrypted 2D vector graphics is proposed in this paper. Firstly, a content owner uses a key to scramble the polar angles of the vertices to encrypt the graphics in the polar coordinate system. Consequently, a watermark embedder maps the encoded watermark segments to different vertices under the control of an embedding key and an HMAC (hash-based message authentication code) function. After that, the polar angle of the vertex is slightly adjusted to embed a watermark. Since the decryption operation does not affect the embedded watermark, the watermark can be extracted both in the plaintext and encrypted domain. Experimental results and analysis show that the proposed scheme can achieve good invisibility and reversibility. It can effectively resist normal operations such as rotation, scaling, translation (RST) and entity reordering, and it has good robustness against malicious attacks such as vertices/entities addition, deletion and modification.

Designing selective encryption (SE) schemes for H.265/HEVC has been attracted much attention with the advent of H.265/HEVC codec in the past two decades. However, the most SE algorithms for H.265/HEVC encrypt the syntax elements in the bypass mode to keep the bit rate. Moreover, the edge region of the video data is not sufficiently protected. To produce large visual distortion and edge loss, a tunable SE scheme for H.265/HEVC based on the chroma intra prediction mode (IPM) and coefficient scrambling is proposed. First, a pseudo-random number sequence is generated by AES-CTR. Then, the prediction, residual, and reconstruction information in the H.265/HEVC encoding process is encrypted by a pseudo-random sequence. It encrypts the syntax elements of context-based adaptive binary arithmetic coding (CABAC) in the bypass mode. Some syntax elements, including chroma IPM in the regular mode, are encrypted as well. To further protect the edge information, a coefficient scrambling is adopted. The edge information of each frame is extracted and the transform units (TUs) are classified according to it. Then, the coefficients of the TUs containing edge are scrambled. Finally, a sign used for marking the type of each TU is embedded into a coefficient. The experimental results and analysis show that the proposed scheme has better visual distortion and subjective evaluation results compared with some existing H.265/HEVC SE algorithms. Meanwhile, users can flexibly use the proposed SE scheme according to encryption performance and bit rate requirements, which is attractive in the scenario of protecting video in cloud servers.

In this paper, a Computer-generated graphics (CG) facial image regeneration scheme for anti-forensics based on generative adversarial network (CGR-GAN) is proposed. The generator of CGR-GAN utilizes a deep U-Net structure, and its discriminator utilizes some stacked convolution layers. Besides, content loss and style loss are both designed to guarantee that the regenerated CG facial images (CGR) retain both the facial profile of the original CG and the characteristics of natural image (NI). Experimental results and analysis demonstrate that the CG facial images regenerated by the proposed anti-forensics scheme can achieve better visual quality compared with those of the existing CG facial image anti-forensics and domain adaptation methods, and it can strike a good balance between visual quality and deception ability.

Currently, much attention has been paid to reversible data hiding (RDH) in an encrypted domain due to the popular deployment of cloud storage. However, nearly all existing RDH schemes in the encrypted domain are proposed for raster images, and very little work has been done to 2D vector graphics, which are represented in real numbers. In this paper, a reversible mapping model for real numbers is first built. It maps the points in Rnto 2snon-intersecting subsets in Rn, which guarantees that s bits can be embedded into each real number. Based on the model, an RDH scheme in encrypted 2D vector graphics is put forward. In the scheme, a user encrypts 2D engineering graphics and stores them in the cloud, and then the cloud service provider can perform information hiding, extraction, and even recover the encrypted 2D vector graphics. For the authorized user, it can acquire the recovered 2D vector graphics from the cloud and obtain their original versions after decryption. For an unauthorized user, he can only acquire the encrypted 2D vector graphics with a hidden message, and only approximate 2D vector graphics can be obtained even if he knows the decryption key but does not know the hiding key. The experimental results and analysis show that it can strike a good balance between security, distortion, and capacity. It provides a new paradigm for RDH in the encrypted domain for the data represented in real numbers.

With the development of 3-D printing and reverse engineering, the protection of intellectual property of 3-D printed objects is becoming a prominent problem. In order to authenticate 3-D printed objects, an authentication scheme based on printing noise and digital signature is proposed. First, the noises introduced in the 3-D printing and observation are investigated. Thereafter, a special authentication mark is designed for extracting the printing noise. Based on this, a 3-D printed object authentication framework is built and it is composed of two processes-registration and verification. In the registration, the printing noise of the authentication mark is extracted and signed by digital signature. While in the verification, the signature is verified and then the printing noise of the authentication mark is extracted. After that, the extracted printing noise is matched with the one acquired in the registration. Experimental results and analysis show that the proposed scheme can reliably accomplish the authentication of the 3-D printed object with high precision and that it can achieve high security and good robustness.

To counter face presentation attack in face recognition system, the chromatic facial texture differences between the real faces and the facial artefacts are fully analyzed, and chromatic co-occurrence of local binary pattern (CCoLBP) is proposed to investigate the inter-channel based information. Based on the principle of presentation attack and its influence on color component of the face image, a face presentation attack detection (PAD) scheme based on CCoLBP is proposed. By combining intra-channel based facial texture and CCoLBP feature, the differences of color distortion and texture distribution between the real faces and the artefacts are characterized. With these features, the detection is accomplished by using a Softmax classifier. Experiments are done with 5 public databases, and the experimental results and analysis indicate the effectiveness of CCoLBP, and it can achieve good performance in cross-database testing. It has great potential in the application of face PAD with real-time requirement.

Morphing attack is becoming a serious challenge for the existing face recognition systems. Aiming at face morphing detection, a novel method is proposed by using Fourier spectrum of sensor pattern noise (FS-SPN). The sensor pattern noise of the facial image is first extracted based on guided image estimation, and the facial quantification statistics, which characterize the specific frequency difference in FS-SPN between the real face image and the morphed image, are obtained. With a linear support vector machine, morphed face image can be detected. Experimental results and analysis show that it outperforms the existing methods in detection accuracy for both complete morphing and splicing morphing.

With the aim of reducing the distortion of traditional partitions, this paper investigates a novel method that partitions every square region into 2nnesting sub-regions. Based on the partition, a low-distortion reversible watermarking for 2D engineering graphics is proposed. The watermark is embedded by mapping the vertices in the original region to its corresponding sub-regions, and it is extracted according to the locations of the mapped vertices. Meanwhile, the original locations of the vertices can be restored by inverse mapping. Furthermore, by constructing a new coordinate system, the rotation, scaling, and translation semi-fragility of the watermarking can be achieved. Experimental results and analysis show that the proposed watermarking achieves good performance in terms of the capacity and semi-fragility, while the imperceptibility of the watermarking is significantly improved in comparison with the existing algorithms under the same conditions.

In order to improve the robustness and capability of resisting image steganalysis, a novel coverless image steganography algorithm based on discrete cosine transform and latent dirichlet allocation (LDA) topic classification is proposed. First, latent dirichlet allocation topic model is utilized for classifying the image database. Second, the images belonging to one topic are selected, and 8 × 8 block discrete cosine transform is performed to these images. Then robust feature sequence is generated through the relation between direct current coefficients in the adjacent blocks. Finally, an inverted index which contains the feature sequence, dc, location coordinates, and image path is created. For the purpose of achieving image steganography, the secret information is converted into a binary sequence and partitioned into segments, and the image whose feature sequence equals to the secret information segments is chosen as the cover image according to the index. After that, all cover images are sent to the receiver. In the whole process, no modification is done to the original images. Experimental results and analysis show that the proposed algorithm can resist the detection of existing steganalysis algorithms, and has better robustness against common image processing and better ability to resist steganalysis compared with the existing coverless image steganography algorithms. Meanwhile, it is resistant to geometric attacks to some extent. It has great potential application in secure communication of big data environment.

In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper.

A novel identification approach for identifying photographic images (PIM) and photorealistic computer graphics (PRCG) is proposed by using multifractal spectrum features of photo response non-uniformity noise (PRNU). 8 dimensions of mul-tifractal spectrum features of PRNU are extracted to represent the subtle differences between them, and the identification is carried out by using a support vector machine (SVM) classifier. Experimental results and analysis indicate that the proposed method can achieve an average identification accuracy of 98.99%, and has good performance in the ratios between training samples and testing samples. Besides, it is robust against some manipulations such as adding noise, JPEG compression and motion blur.

ABSTRACT Reversible watermark is suitable for the hosts with high precision requirement. However, the existed reversible watermark methods are mainly for raster images, and the reversible watermark schemes for vector graphics still have the defects such as low capacity and significant distortion. To counterstrike these situations, a reversible watermark scheme for two‐dimensional vector map based on reversible contrast mapping is proposed. First, the coordinates of the vertices are chosen according to the data precision requirements; then, the data of two‐dimensional vector maps are preprocessed to reduce distortion. After that, the encrypted watermark is embedded into the relative coordinates by using the reversible contrast mapping transform. Experimental results and analysis show that the proposed scheme can achieve higher payload, better reversibility, and invisibility than those of the existed schemes based on difference expansion, discrete cosine transform, and so on. It has great potential in the application of map data authentication and secure communication. Copyright © 2012 John Wiley & Sons, Ltd.

With the increase of terrorist and criminal activities, closed circuit television (CCTV) is widely used on many occasions. However, abuse of surveillance video may result in the leakage of personal privacy. To protect the privacy in the video of CCTV, an encryption scheme for region of interest (ROI) of H.264 video based on flexible macroblock ordering (FMO) and chaos is proposed in this paper, where human face regions are selected as an example of ROI. First, the human face regions in the video are detected and extracted. Then, they are mapped into slice groups by using FMO technology in H.264. After that, these regions are encrypted using selective video encryption based on chaos. Experimental results and analysis show that the proposed scheme can effectively protect the private information of H.264 video and, therefore, can strike a good balance among the security, encryption efficiency, and coding performance. It has great potential to be used in the privacy protection of the video of CCTV.

The bit error rate (BER) performance of chaos shift keying is improved for an additive white Gaussian noise channel by applying a trimming operation to the Chebyshev polynomial function of order 2 and the piecewise linear map to make the input source more Gaussian distributed. Analytical expressions for the improved BER are also derived based on the curve-fitting approximation. Numerical results show that the proposed method can achieve performance gains of up to 3.5 dB in signal-to-noise ratio over the conventional method without trimming.