Integrity Of Image Content Research Articles

DMCVS: Decomposed motion compensation‐based video stabilization

AbstractWith the popularity of handheld devices, video stabilization is becoming increasingly important. In previous studies, many methods have been proposed to stabilize shaky videos. However, these methods fail to balance between image content integrity and stability. Some methods sacrifice image content for better stability. Other methods ignore the subtle jitters, which leads to poor stability. This work innovatively proposes a video stabilization method based on decomposed motion compensation. First, a grid‐based motion statistics method is adopted for motion estimation, which obtains more accurate motion vectors according to matched likelihood estimates. Then, the motion compensation is inherently decomposed into two parts: linear motion compensation and auxiliary motion compensation. Linear motion compensation removes complex jitter by constructing linear path constraints to obtain a more stable camera path. Auxiliary motion compensation uses a moving average filter to remove the high‐frequency jitter as a supplement and preserve more image content. The two components are combined with individual weights to derive the final transform matrix and warp the original frames. Experimental results show that our method outperforms the previous methods on NUS and DeepStab datasets qualitatively and quantitatively.

Optical color fragile watermark based on pixel-free expansion visual cryptography

In recent years, with the continuous development of computer technology, it has brought convenience to people to obtain image information. However, at the same time, the falsification and theft of image information have also emerged, so information security has received increasing attention. When images are used for medicine, military, court, and other purposes, it is necessary to ensure the authenticity and integrity of the image content. Fragile watermarks are used to verify the authenticity and integrity of image content due to their sensitivity to tampering. The watermark information is embedded in the image and integrated with the image. When it is necessary to detect the authenticity and integrity of image information, the extracted watermark can be used to determine whether the image is reliable and complete. Therefore, we propose an optical color fragile watermarking system based on pixel-free expansion visual cryptography. On the one hand, encoding watermark images by using pixel-free expansion visual cryptography avoids pixel expansion issues caused by visual cryptography, allowing for the selection of color host images with the same pixel size as the watermark image in the future, greatly reducing the network bandwidth and storage space occupied during transmission. On the other hand, phase recovery algorithm is used to process the encoded watermark image to obtain phase information for embedding into the host image, further improving the security of the watermark image in an optical way. The feasibility and imperceptibility of the proposed optical color fragile watermark are verified through computer simulation, and its good fragility is verified through a series of simulation attack experiments. It can sensitively detect image tampering in the face of common attacks such as noise pollution, rotation, motion blur processing, filtering, etc.

Self-recovery reversible image watermarking algorithm.

The integrity of image content is essential, although most watermarking algorithms can achieve image authentication but not automatically repair damaged areas or restore the original image. In this paper, a self-recovery reversible image watermarking algorithm is proposed to recover the tampered areas effectively. First of all, the original image is divided into homogeneous blocks and non-homogeneous blocks through multi-scale decomposition, and the feature information of each block is calculated as the recovery watermark. Then, the original image is divided into 4×4 non-overlapping blocks classified into smooth blocks and texture blocks according to image textures. Finally, the recovery watermark generated by homogeneous blocks and error-correcting codes is embedded into the corresponding smooth block by mapping; watermark information generated by non-homogeneous blocks and error-correcting codes is embedded into the corresponding non-embedded smooth block and the texture block via mapping. The correlation attack is detected by invariant moments when the watermarked image is attacked. To determine whether a sub-block has been tampered with, its feature is calculated and the recovery watermark is extracted from the corresponding block. If the image has been tampered with, it can be recovered. The experimental results show that the proposed algorithm can effectively recover the tampered areas with high accuracy and high quality. The algorithm is characterized by sound visual quality and excellent image restoration.

Color-Decoupled Photo Response Non-Uniformity for Digital Image Forensics

The last few years have seen the use of photo response non-uniformity noise (PRNU), a unique fingerprint of imaging sensors, in various digital forensic applications such as source device identification, content integrity verification, and authentication. However, the use of a color filter array for capturing only one of the three color components per pixel introduces color interpolation noise, while the existing methods for extracting PRNU provide no effective means for addressing this issue. Because the artificial colors obtained through the color interpolation process are not directly acquired from the scene by physical hardware, we expect that the PRNU extracted from the physical components, which are free from interpolation noise, should be more reliable than that from the artificial channels, which carry interpolation noise. Based on this assumption we propose a couple-decoupled PRNU (CD-PRNU) extraction method, which first decomposes each color channel into four sub-images and then extracts the PRNU noise from each sub-image. The PRNU noise patterns of the sub-images are then assembled to get the CD-PRNU. This new method can prevent the interpolation noise from propagating into the physical components, thus improving the accuracy of device identification and image content integrity verification.

SPEEDING UP BEST NEIGHBORHOOD MATCHING ALGORITHM FOR HIGH-DEFINITION IMAGE ON GPU PLATFORM

Error concealment restores the visual integrity of image content that has been damaged due to a bad network transmission. Best neighborhood matching (BNM) is an effective image-recovery method that exploits the information redundancy in a block-coded broken image to find similar content that it then uses to repair or conceal errors. On a high-definition image, BNM is traditionally implemented sequentially, which requires a relatively long time and thus is not suitable for real-time or high-volume use. In this paper, we analyze the data access patterns of the BNM algorithm, and exploit a graphics process unit (GPU) platform to speed up the execution through a parallel implementation. We compare and combine several different GPU optimization methods (coalesced global memory access, shared memory, register files, etc.), and propose an improvement to the parallel BNM algorithm. Experimental results show that our approach can speed up BNM 62 times over the sequential approach without any obvious loss of accuracy.

A Fragile Digital Image Authentication Scheme Inspired by Wet Paper Codes

Image authentication is an important research topic of maintaining the integrity of digital image contents. Fragile image authentication is the technique for achieving the goal of image content integrity maintenance. This article presents a fragile image authentication scheme based on the concept of wet paper codes. The proposed scheme modifies dry pixels on an image to conceal an image signature. The proposed authentication scheme can exactly detect the tampered area on a tampered image. For saving computation cost of signature embedding, an exclusive-or operation is used in the proposed authentication scheme. The experimental results show that the proposedmethod not only has good visual quality of an authorized image but also successfully detects tampered areas on a tampered image.

Region-Level Image Authentication Using Bayesian Structural Content Abstraction

Image authentication (IA) verifies the integrity of image content by detecting malicious modifications. A good IA system should be able to tolerate noncontent-changing operations (NCOs) robustly, and detect content-changing operations (COs) sensitively. Most existing IA methods realize either bit-level or pixel-level authentication; thus, they can tolerate only particular and limited kinds of NCOs. In this paper, we propose an unsupervised region-level IA scheme named Bayesian structural content abstraction (BaSCA), which is capable of tolerating a wide and dynamic range of NCOs and can sensitively detect real COs. We model image structural content using the net-structured Markov Pixon random field (NS-MPRF), from which we derive the size-controllable BaSCA signature. Furthermore, to support dynamic NCO/CO partition, we present an analogous mean-shift algorithm to iteratively optimize the BaSCA signature in the user-defined NCO space. Both theoretical analysis and experimental results demonstrate that our BaSCA scheme has much less false positive and comparable false negative probability, as compared to state-of-the-art IA methods.