Steganography Using Reversible Texture Synthesis Based on Seeded Region Growing and LSB

Steganography technology has been widely used in data transmission with secret information.However,the existing steganography has the disadvantages of low hidden information capacity,poor visual effect of cover images,and is hard to guarantee security.To solve these problems,steganography using reversible texture synthesis based on seeded region growing and LSB is proposed.Secret information is embedded in the process of synthesizing texture image from the existing natural texture.Firstly,we refine the visual effect.Abnormality of synthetic texture cannot be fully prevented if no approach of controlling visual effect is applied in the process of generating synthetic texture.We use seeded region growing algorithm to ensure texture’s similar local appearance.Secondly,the size and capacity of image can be decreased by introducing the information segmentation,because the capacity of the secret information is proportional to the size of the synthetic texture.Thirdly,enhanced security is also a contribution in this research,because our method does not need to transmit parameters for secret information extraction.LSB is used to embed these parameters in the synthetic texture.

Image completion algorithm based on texture synthesis

A new algorithm is proposed for completing the missing parts caused by the removal of foreground or background elements from an image of natural scenery in a visually plausible way. The major contributions of the proposed algorithm are： （1） for most natural images, there is a strong orientation of texture or color distribution. So a method is introduced to compute the main direction of the texture and complete the image by limiting the search to one direction to carry out image completion quite fast; （2） there exists a synthesis ordering for image completion. The searching order of the patches is defined to ensure the regions with more known information and the structures should be completed before filling in other regions; （3） to improve the visual effect of texture synthesis, an adaptive scheme is presented to determine the size of the template window for capturing the features of various scales. A number of examples are given to demonstrate the effectiveness of the proposed algorithm.

Constrained circular-patch-based texture synthesis

Texture synthesis has been developed for several years.The traditional technique can generate a larger image from a small image while avoid feeling of repetition or uncontinuity.Some constrained synthesis methods which can synthesize image according to special location demand or other demands have been also proposed in recent years.However,in general,these constrained texture synthesis methods are simple and have few controllable factors to meet user＇s diverse needs.To control multiple-sample texture synthesis more flexibly in various aspects such as synthesis location,proportion and semantic objects,we present an interactive texture synthesis approach based on circular patches and constrained by objects according to a certain ratio.With this approach,source exemplars and the target image are firstly divided into several regions with different characters.Users can click the blocks in the source exemplars and the want-to-be-synthesized region in the target image,and then texture in the target image is synthesized with the corresponding regions in the source exemplars.In the process of texture synthesis,circular patch instead of square patch is used to eliminate the aliasing phenomena.Images are synthesized from multiple sample images with ratio constraint and experiments on images show that our approach can get effective results of ratio-constrained multi-sample synthesis.

Real-Time Texture Synthesis Using s-Tile Set

This paper presents a novel method of generating a set of texture tiles from samples, which can be seamlessly tiled into arbitrary size textures in real-time. Compared to existing methods, our approach is simpler and more advantageous in eliminating visual seams that may exist in each tile of the existing methods, especially when the samples have elaborate features or distinct colors. Texture tiles generated by our approach can be regarded as single-colored tiles on each orthogonal direction border, which are easier for tiling and more suitable for sentence tiling. Experimental results demonstrate the feasibility and effectiveness of our approach.

Orientation Field Guided Texture Synthesis

We present a framework for example-based texture synthesis with feature directions aligned to vector fields with 2-way rotational symmetry, also known as orientation fields. Through a simple variational formulation, the framework allows the user to design the orientation field with intuitive controls, by interactively manipulating singularities and field directions. The resulting field is then used to guide a parallel synthesis. Our contribution is twofold： a design tool for orientation fields with a natural boundary condition, and a parallel texture synthesis adapted specifically for using such fields in feature alignment. We demonstrate the advantages of the procedure through examples on planar and curved patches with trivial topology.

Co-occurrence based texture synthesis

As image generation techniques mature,there is a growing interest in explainable representations that are easy to understand and intuitive to manipulate.In this work,we turn to co-occurrence statistics,which have long been used for texture analysis,to learn a controllable texture synthesis model.We propose a fully convolutional generative adversarial network,conditioned locally on co-occurrence statistics,to generate arbitrarily large images while having local,interpretable control over texture appearance.To encourage fidelity to the input condition,we introduce a novel differentiable co-occurrence loss that is integrated seamlessly into our framework in an end-to-end fashion.We demonstrate that our solution offers a stable,intuitive,and interpretable latent representation for texture synthesis,which can be used to generate smooth texture morphs between different textures.We further show an interactive texture tool that allows a user to adjust local characteristics of the synthesized texture by directly using the co-occurrence values.

Texture synthesis via the matching compatibility between patches

A new patch-based texture synthesis method is presented in this paper. By the method, a set of patches that can be matched with a sampled patch for growing textures effectively, called the matching compatibility between patches, is generated first for each patch, and the set is further optimized by culling the patches that may cause synthesis conflicts. In this way, similarity measurement calculation for selecting suitable patches in texture synthesis can be greatly saved, and synthesis conflicts between neighbouring patches are substantially reduced. Furthermore, retrace computation is integrated in the synthesis process to improve the texture quality. As a result, the new method can produce high quality textures as texture optimization, the best method to date for producing good textures, and run in a time complexity linear to the size of the output texture. Experimental results show that the new method can interactively generate a large texture in 1024 × 1024 pixels, which is very difficult to achieve by existing methods.

Transition Texture Synthesis

Synthesis of transition textures is essential for displaying visually acceptable appearances on a terrain. This investigation presents a modified method for synthesizing the transition texture to be tiled on a terrain. All transition pattern types are recognized for a number of input textures. The proposed modified patch-based sampling texture synthesis approach, using the extra feature map of the input source and target textures for patch matching, can synthesize any transition texture on a succession pattern by initializing the output texture using a portion of the source texture enclosed in a transition cut. The transition boundary is further enhanced to improve the visual effect by tracing out the integral texture elements. Either the Game of Life model or Wang tiles method are exploited to present a good-looking profile of successions on a terrain for tiling transition textures. Experimental results indicate that the proposed method requires few input textures, yet synthesizes numerous tileable transition textures, which are useful for obtaining a vivid appearance of a terrain.

Research and Implementation of the Practical Texture Synthesis Algorithms

How to generate pictures real and esthetic objects is an important subject of computer graphics. The techniques of mapping textures onto the surfaces of an object in the 3D space are efficient ap- proaches for the purpose.We developed and implemented algorithms for generating objects with appear ances stone,wood grain,ice lattice,brick,doors and windows on Apollo workstations. All the algorithms have been incorporated into the 3D geometry modelling system(GEMS)developed by the CAD Center of Tsinghua University.This paper emphasizes the wood grain and the ice lattice algorithms.

Development of Texture Mapping Approaches for Additively Manufacturable Surfaces

manufacturing(AM)technologies have been recognized for their capability to build complex components and hence have ofered more freedom to designers for a long time.The ability to directly use a computer-aided design(CAD)model has allowed for fabricating and realizing complicated components,monolithic design,reducing the number of components in an assembly,decreasing time to market,and adding performance or comfort-enhancing functionalities.One of the features that can be introduced for boosting a component functionality using AM is the inclusion of surface texture on a given component.This inclusion is usually a difcult task as creating a CAD model resolving fne details of a given texture is difcult even using commercial software packages.This paper develops a methodology to include texture directly on the CAD model of a target surface using a patch-based sampling texture synthesis algorithm,which can be manufactured using AM.Input for the texture generation algorithm can be either a physical sample or an image with heightmap information.The heightmap information from a physical sample can be obtained by 3D scanning the sample and using the information from the acquired point cloud.After obtaining the required inputs,the patches are sampled for texture generation according to non-parametric estimation of the local conditional Markov random feld(MRF)density function,which helps avoid mismatched features across the patch boundaries.While generating the texture,a design constraint to ensure AM producibility is considered,which is essential when manufacturing a component using,e.g.,Fused Deposition Melting(FDM)or Laser Powder Bed Fusion(LPBF).The generated texture is then mapped onto the surface using the developed distance and angle preserving mapping algorithms.The implemented algorithms can be used to map the generated texture onto a mathematically defned surface.This paper maps the textures onto fat,curved,and sinusoidal surfaces for illustration.After the texture mapping,a stereolithography(STL)model is generated with the desired texture on the target surface.The generated STL model is printed using FDM technology as a fnal step.

Constructive Texture Steganography Based on Compression Mapping of Secret Messages

This paper proposes a new constructive texture synthesis steganographic scheme by compressing original secret messages.First,we divide the original message into multiple bit blocks,which are transferred to decimal values and compressed into small decimal values by recording their interval sign characters.Then,a candidate pattern is generated by combining the given source pattern and boundary extension algorithm.Furthermore,we segment the candidate pattern into multiple candidate patches and use affine transformation algorithm to locate secret positions on a blank canvas,which are used to hide the sign characters by mapping the candidate patches.Finally,we select the candidate patches with minimal mean square error to represent secret bits to generate stego image by image quilting.Extensive experiments demonstrate that compared with existing texture steganographic methods,our method has a better visual quality,higher embedding capacity and security performance,while maintaining strong anti-steganalysis capability.

Method of Direct Texture Synthesis on Arbitrary Surfaces

A direct texture synthesis method on arbitrary surfaces is proposed in thispaper. The idea is to recursively map triangles on surface to texture space until the surface iscompletely mapped. First, the surface is simplified and a tangential vector field is created overthe simplified mesh. Then, mapping process searches for the most optimal texture coordinates intexture sample for each triangle, and the textures of neighboring triangles are blended on the mesh.All synthesized texture triangles are compressed to an atlas. Finally, the simplified mesh issubdivided to approach the initial surface. The algorithm has several advantages over formermethods: it synthesizes texture on surface without local parameterization; it does not needpartitioning surface to patches; and it does not need a particular texture sample. The resultsdemonstrate that the new algorithm is applicable to a wide variety of texture samples and anytriangulated surfaces.

Embedding Image Through Generated Intermediate Medium Using Deep Convolutional Generative Adversarial Network

Deep neural network has proven to be very effective in computer vision fields.Deep convolutional network can learn the most suitable features of certain images without specific measure functions and outperform lots of traditional image processing methods.Generative adversarial network(GAN)is becoming one of the highlights among these deep neural networks.GAN is capable of generating realistic images which are imperceptible to the human vision system so that the generated images can be directly used as intermediate medium for many tasks.One promising application of using GAN generated images would be image concealing which requires the embedded image looks like not being tampered to human vision system and also undetectable to most analyzers.Texture synthesizing has drawn lots of attention in computer vision field and is used for image concealing in steganography and watermark.The traditional methods which use synthesized textures for information hiding mainly select features and mathematic functions by human metrics and usually have a low embedding rate.This paper takes advantage of the generative network and proposes an approach for synthesizing complex texture-like image of arbitrary size using a modified deep convolutional generative adversarial network(DCGAN),and then demonstrates the feasibility of embedding another image inside the generated texture while the difference between the two images is nearly invisible to the human eyes.

基于图像分解的分层图像修复(英文)

We propose a layered image inpainting scheme based on image decomposition. The damaged image is first decomposed into three layers： cartoon, edge, and texture. The cartoon and edge layers are repaired using an adaptive offset operator that can fill-in damaged image blocks while preserving sharpness of edges. The missing information in the texture layer is generated with a texture synthesis method. By using discrete cosine transform （DCT） in image decomposition and trading between resolution and computation complexity in texture synthesis, the processing time is kept at a reasonable level.

Interactive texture design and synthesis from mesh sketches

Geometry mesh introduces user control into tex- ture synthesis and editing, and brings more variations in the synthesized results. But still two problems related remain in need of better solutions. One problem is generating the meshes with desired size and pattern efficiently from easier user inputs. The other problem is improving the quality of synthesized results with mesh information. We present a new two-step texture design and synthesis method that addresses these two problems. Besides example texture, a small piece of mesh sketch drawn by hand or detected from example texture is input to our algorithm. And then a mesh synthesis method of geometry space is provided to avoid optimizations cell by cell. Distance and orientation features are introduced to im- prove the quality of mesh rasterization. Results show that with our method, users can design and synthesize textures from mesh sketches easily and interactively.

Multi-Layer Synthesis of HighlyStructured Texture

In this paper, a novel concept of multilayer synthesis and a general framework for texture synthesis method are presented. Within this framework, we first decompose the texture into the supposed pattern layer and material layer in the frequency domain by an E-texton extracting algorithm, then manipulate and extend them respectively according to their own personalities, and finally merge the newly synthesized pattern layer and material layer again to generate the final output. Experiment results show that our method not only greatly improves the synthesis quality for those cases that single-layer synthesis cannot handle well but also provides an ability of achieving various special synthesis effects.

A survey of the state-of-the-art in patch-based synthesis

This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar imagery. This line of research originated from an area called "texture synthesis", which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionally, recent research has focused on customizing the synthesis process for particular problem domains, such as synthesizing artistic or decorative brushes, synthesis of rich materials, and synthesis for 3D fabrication. This report investigates recent papers that follow these themes, with a particular emphasis on papers published since 2009,when the last survey in this area was published. This survey can serve as a tutorial for readers who are not yet familiar with these topics, as well as provide comparisons between these papers, and highlight some open problems in this area.

Accelerated Parallel Texture Optimization

Texture optimization is a texture synthesis method that can efficiently reproduce various features of exemplar textures. However, its slow synthesis speed limits its usage in many interactive or real time applications. In this paper, we propose a parallel texture optimization algorithm to run on GPUs. In our algorithm, k-coherence search and principle component analysis （PCA） are used for hardware acceleration, and two acceleration techniques are further developed to speed up our GPU-based texture optimization. With a reasonable precomputation cost, the online synthesis speed of our algorithm is 4000＋ times faster than that of the original texture optimization algorithm and thus our algorithm is capable of interactive applications. The advantages of the new scheme are demonstrated by applying it to interactive editing of flow-guided synthesis.

Autocompletion of repetitive stroking with image guidance

Image-guided drawing can compensate for a lack of skill but often requires a significant number of repetitive strokes to create textures.Existing automatic stroke synthesis methods are usually limited to predefined styles or require indirect manipulation that may break the spontaneous flow of drawing.We present an assisted drawing system to autocomplete repetitive short strokes during a user's normal drawing process.Users draw over a reference image as usual;at the same time,our system silently analyzes the input strokes and the reference to infer strokes that follow the user's input style when certain repetition is detected.Users can accept,modify,or ignore the system's predictions and continue drawing,thus maintaining fluid control over drawing.Our key idea is to jointly analyze image regions and user input history to detect and predict repetition.The proposed system can effectively reduce the user's workload when drawing repetitive short strokes,helping users to create results with rich patterns.