Secure self-recovery watermarking scheme for error concealment and tampering detection
© The Author(s) 2016
Received: 21 April 2016
Accepted: 8 September 2016
Published: 22 September 2016
In this paper, we present a method for protecting and restoring lost or tampered information in images or videos using secure watermarks. The proposed method consists of a combination of techniques that are able to detect image and video manipulations. Contrary to most existing watermarking schemes, the method can identify the exact position of the tampered region. Furthermore, the method is capable of restoring the manipulated information and retrieve the original content. This set of capabilities make it possible to use the proposed method in error concealment and digital tampering applications.
The proposed method is employed as both an error concealment algorithm and a tampering detection algorithm. The proposed method is divided into two stages. At the encoder side, the method generates a binary version (watermark) of the original picture (image or video frame) using a halftoning technique. Then, a quantization index modulation technique is used to embed this watermark into the protected picture. At the decoder side, after the lost or tampered regions are identified, the original content is recovered by extracting the watermark corresponding to the affected areas. An inverse halftoning algorithm is used to convert the dithered version of the picture into a good-quality multi-level approximation of the original content.
First, we test the method in error concealment applications, using a set of still images and H.264 videos. Then, we test the proposed method for tampering detection and content retrieval applications, again considering both images and videos. We compare the proposed method with several other several state-of-the-art algorithms. The results show that the proposed method is fast, robust, and accurate.
Our results show that we can use a single approach to tackle both error concealment and tampering detection problems. The proposed method provides high levels of security, high detection accuracy, and recovery capability, and it is robust to several types of attacks.
KeywordsQuantization index modulation Watermarking Error concealment Tampering detection
The flexibility of digital images and videos is both a blessing and a curse. Digital technologies make it possible to create high-quality pictures, animations, games, and special effects with an amazing realism. Digital pictures (images and videos) can be enhanced, compressed, transmitted, translated across different standards, and displayed in a variety of devices. Because of the significant advances in compression and transmission techniques, it is possible to deliver high-quality visual content to the end user in many different ways. As a consequence, a variety of delivery services have been created in the last years, such as direct TV broadcast satellite, digital broadcast television, and IP-based video streaming.
In content delivery applications, video and image signals are transmitted in a compressed format  and they are divided into packets before transmission. Unfortunately, during the transmission over wired and wireless channels, some packets may be lost or delayed. These transmission losses cause various types of visible degradations that may affect the quality of the perceived content. In video signals, transmission losses cause spatial and temporal degradations. These degradations may again affect the overall perceived video quality, what influences the acceptability and popularity of the application.
To minimize the effect of transmission errors, error resiliency techniques are often used. These techniques can be classified as forward, interactive, and concealment techniques . Forward techniques add redundant data to the video. Although this increases the amount of data to be transmitted, forward methods have the advantage of not requiring interaction between the encoder and the decoder. On the other hand, interactive techniques use a feedback channel that allows the decoder to request the encoder to resend data. A transmission of redundant data avoids wasting resources but may introduce delays and locks that make it unsuitable for real-time video streaming applications . In contrast to these approaches, concealment techniques usually do not increase the amount of data to be transmitted or require a side channel . They can be implemented with or without a modification to the encoder. Therefore, concealment techniques are very attractive for real-time applications that require a low bit-rate and delay .
Although there are several possible approaches, most error concealment algorithms use error prediction techniques, like interpolation [6, 7]. For example, Lin et al.  use an advanced interpolation technique. Their algorithm makes a partition decision considering information from previous frames. Ranjan et al.  propose a method that uses an affine transformation that uses a speed-up robust features (SURF) algorithm to find a relation between current and previous frames and, then, predict the lost data. Koloda et al.  proposes an error concealment technique that is based on sparse linear prediction.
Other approaches that promote error resiliency combine concealment and forward techniques. Basically, the error is concealed using information previously embedded into the original signal, what does not increase the amount of data to be transmitted. If some part of the data is lost during the transmission, the embedded information can be extracted and used to recover the original data. The signal acts as a channel (host) to transmit the hidden data. Often, this approach is related to dirty paper coding , specially when the data hiding technique is the quantization index modulation (QIM) algorithm [12, 13].
The use of data hiding reduces the decoder’s complexity. One example of this approach is the technique proposed by Yin et al.  that uses data hiding to embed a set of features extracted from the signal at the encoder. Similarly, Chung et al.  use a reversible data hiding technique to insert motion vectors (MVs) into zero quantized discrete cosine transform (QDCT) coefficients and perform intra-frame error concealment for H.264/AVC coded video sequences. A general method for recovering missing DCT coefficients in DCT-transformed images is proposed by Li et al. . The work of Xu et al.  is an improvement of Chung’s method, which exploits the number of coefficients that needs to be modified to extract the hidden data. Their work considers the residual blocks produced by the data hiding algorithm. Wang et al.  present an image restoration method that is based on on a linear optimization model and restores part of the image from structured side information.
A slightly different approach was proposed by Adsumilli et al. . Their technique uses a spread-spectrum watermarking algorithm to embed a dithered version of a picture frame into the host video. Navak et al.  improved Adsumilli’s technique by adding motion estimation vectors with edge-correlated information. Both schemes embed a low-resolution version of the picture or video frame into the original video content using a spread-spectrum watermarking technique. At the receiver side (decoder), the embedded watermark is extracted from the received video frame and is used as a reference for reconstructing the original signal. Unfortunately, the disadvantage of these two works is that the quality of the reconstruction is low because of the low capacity of the spread-spectrum data hiding scheme.
In addition to error concealment techniques, another important concern for image and video applications is tamper detection and copyright protection. Nowadays, there are several softwares for editing images and videos, making it very easy to alter (tamper) content without leaving any clear sign. Several techniques have been proposed with the goal of detecting tampered areas and reconstructing the original content . These techniques share similar goals with the error concealment algorithms. The difference is that in tampering detection the “non-original” areas are deliberately modified, while in the error concealment case the “non-original” areas are damaged or lost. Tampering detection techniques can be divided into no-reference, reduced-reference, and full-reference approaches. Full-reference approaches require the full original picture to determine whether the picture is tampered. Reduced-reference approaches need some information of the original to determine the tampering but do not require the full original picture. No-reference approaches detect tampering using only the tested picture, i.e. they do not require any information from the original content. For transmission applications, the original is not available. Therefore, no-reference approaches are the most adequate ones. Most no-reference techniques are specialized in detecting only one type of tampering [22, 23], what is not generally not very useful in practical applications.
Similarly to the error concealment techniques proposed by Adsumilli et al.  and Navak et al. , one popular no-reference tampering detection approach consists of using data hiding. Imaizumi et al.  detect and locate tampered areas in images using a reversible data hiding and a low cost scheme with efficiently multiplexed layers. The method proposed by Xu et al.  embeds data directly into the encrypted H.264/AVC video bitstream. Phadikar et al. , on the other hand, proposes a tamper detection and correction scheme based on a novel semi-fragile data hiding technique. This method is based on an integer wavelet transform and a quantization index modulation (QIM) algorithm. Tong et al.  also use a watermarking algorithm to detect and localize tampered areas. Lin et al.  present an authentication and recovery method for tampered images.
Among the methods available in the literature, few approaches address the problem of restoring the original content with good quality. Dadkhah et al.  propose an effective tamper detection that uses a singular value decomposition (SVD) in a self-recovery algorithm. In a more recent work, Som et al.  propose a discrete wavelet transform (DWT) watermarking scheme for tamper detection, localization, and restoration targeted at cropping attacks. Although these methods have a good performance (in terms of quality), they are very specialized and only work for one or two types of attacks.
In this paper, we present a method that works both as an error concealment technique and a tampering detector with restoration capability. The proposed method is based on watermarking and halftoning techniques. At the encoder side, the method generates a binary version of the original picture (image or video frame) using a halftoning technique. Then, a watermarking technique is used to embed this mark into the host content. The watermarking technique used is a modification of the QIM that achieves a higher data hiding capacity than traditional methods. For tampering detection, a ciphered key is also embedded into the host video to allow spatial and temporal localization of tampered regions. At the decoder side, after the modified (degraded or tampered) regions are identified, the original content is recovered by extracting the halftone image corresponding to the affected areas. An improved inverse halftoning algorithm is used to convert the dithered picture into a good quality approximation (colored) of the original picture. The quality of the reconstructed areas is higher than for other algorithms available in the literature. The tampering method is generic enough to recover tampered areas independently of the type of attack. Finally, the proposed method is among the few methods in the literature that also work for video signals, detecting and reconstructing (spatially and temporally) modified regions in videos with a good perceived quality and few temporal artifacts.
The rest of this paper is divided as follows. In the “Halftoning” section, the halftoning method is described. In the “Watermarking embedding” section, the watermarking embedding stage is explained. In the “Watermarking extraction” and “Inverse halftoning” sections, the watermarking extraction and inverse halftoning stages are detailed. In the “Error concealment algorithm” and “Tampering detection algorithm” sections, we describe the implementation and simulation results of the two main applications of the proposed system: error concealment and tampering detection algorithm. Finally, the “Conclusions” section presents the conclusions.
Halftoning is a technique for converting multi-level images into binary images using patterns of white and black dots . This technique creates the illusion of seeing multiple intensity levels in a binary image, what makes it suitable for applications where only a reduced number of levels is available, such as newspapers, fax machines, and document printing processes.
In this work, a halftoning algorithm is used to generate a dithered version of each picture (still image or video frame), which is later embedded into the host picture itself. At the decoder, if any loss or tampered area is detected, the dithered version of the picture is recovered and used to restore the content back to its original state. Therefore, the quality of the restored image or video depends strongly on the efficiency of the halftoning algorithm. One of the contributions of this work is the design of a halftoning and an inverse halftoning algorithms that are able to generate simple dithered images that can be later inverted with a good quality.
To promote high data hiding capacity, we propose a combinatorial dispersed-dot dithering pattern. This dithering method is capable of generating all combinations of bits necessary to represent a number of intensity levels [32, 33]. This way, we can increase the number of mapped intervals without increasing the size of the dot-pattern matrix. For example, using a 3 × 3 Bayesian matrix to generate a dispersed-dot dithering, we can represent 10 distinct intensity levels. On the other hand, using a 3 × 3 combinatorial matrix allows for up to 512 intensity levels.
where x and y are the horizontal and vertical spatial coordinates, respectively, c refers to the color channel (1≤c≤3), and I Q is the resulting dithered image. To enable restoration, up to 3 bits can be embedded in each color channel without causing visible degradations . This means that the dithered mark (I Q ) is represented with a total of 9 bits per pixel. Then, we substitute each value of I Q (x,y,c) by the corresponding 3-bit combinatorial dispersed dot patterns.
For videos signals, we also perform an embedding temporal distribution by inserting the mark corresponding to the current picture frame in a previous picture frame, located 1 s before. By distributing the mark spatially and temporally, a region does not store the mark necessary to restore it, what increases the probability that the algorithm is able to restore the content to its original version. The mark is also encrypted applying a XOR cipher to protect it from being extracted by an unauthorized user.
To detect if a picture region is lost or tampered, the extracted watermark is compared with the corresponding “host” content. For tampering detection, we extract the watermark and compute its inverse halftoning version. By computing the structural similarity (SSIM) between blocks of inverse halftoning and host content, it is expected that SSIM is higher for blocks where there are no tampers. On the other hand, if the image is tampered, the structural similarity between host and restored blocks are lower. For error concealment, the position of the lost areas are identified by the decoder and the damaged areas are substituted by the recovered watermark after an inverse halftoning process.
Given that I th is the dithered picture frame, D(p) is the distribution of the area surrounding the pixel p in I dth. To reconstruct an 8-bit pixel from the dithered picture, we first calculate the local distribution D(p) for all pixels in I dth. From this distribution, we find the corresponding mapped interval that contains the most probable pixel value in the corresponding color channel, according to the indices of the dot patterns. Once this interval is found, we randomly select a value within it, generating a slightly noisy picture I inv.
Error concealment algorithm
We used the techniques described in the previous sections to design an error concealment algorithm. The algorithm is designed to be integrated into a compression codec but can also be used independently of the codec technology. Since decoders are able to identify which packets were lost, there is no need for a key code. To increase the quality of restored areas, for each color channel, we used dithered watermarks of 3 bits (see the “Methods” section, Fig. 2 b).
Tampering detection algorithm
Results and discussion
In this section, we present the simulations of the error concealment algorithm and the tampering detection algorithm, for videos and images.
Quality of watermarking algorithm
UQI, PSNR and SSIM values calculated between original and watermarked videos
Error concealment for images
Error concealment for videos
Second, we test the ability of the proposed algorithm to recover lost packets. The block diagram of this error concealment algorithm for videos is depicted in Fig. 5. We use publicly available videos in YUV 4:4:4 color CIF (352 × 288, progressive) format, with around 300 frames each. The videos are “Foreman,” “Mobile,” “Carphone,” and “Suzie” . We embed the proposed error concealment algorithm into a video stream. In order to simulate packet losses into a given bitstream, we use a simple model that simulates packet losses over error-prone channels. The following packet loss rates (PLR) are used: 0.5, 1, 3, 5, and 10 %.
Although the error concealment methods tested in the previous section (KMMSE, WTM, VC, and XFSE) perform well for images, they are not designed to conceal errors in compressed videos. They are designed to conceal errors only within homogeneous spatial regions and, therefore, cannot recover lost packets because these packets may contain information from several frame blocks distributed along several frames. Given the inability to compare the proposed method with these techniques, we compare it only with the standard H.264 error concealment algorithm.
Tampering detection in images
We test the proposed algorithm using still images with different characteristics: high-detailed, low-detailed, color, grayscale, documents, landscape, faces and people images, etc. Different kinds of attacks are applied to these images: blurring of selected/random areas, noise addition, cut-and-paste, region deletion, and resizing.
Tampering detection in videos
We also test the performance of the algorithm for tampering detection and recovery of digital videos. As in the previous examples, we used publicly available videos in YUV 4:4:4 color CIF (352 × 288, progressive) format with around 300 frames each, downloaded from the Video Trace Library  and from the Consumer Digital Video Library . We tested the following attacks: blurring of selected areas, cut-and-paste, region-deletion, and object-addition. Again, for all test cases, we were able to detect tampered regions in 100 % of the cases. In terms of reconstruction, the algorithm was able to recover tampered regions with good quality.
Mean efficiency of spatial detections per frame for some methods (%)
This paper presents a secure variable-capacity self-recovery watermarking scheme. In the proposed scheme, it is possible to implement both an error concealment algorithm and a tampering detection algorithm. The scheme is based on watermarking and halftoning techniques. In order to increase the data hiding capacity, this work proposed a simple modification of the QIM watermarking algorithm. To obtain higher quality restored areas, improved inverse halftoning algorithms are also proposed. A secret key code is embedded to the host content to identify the spatial and temporal positions of tampered regions, taking advantage of the lower sensitivity of the HVS to degradations in the blue color channel. Above all, the proposed scheme not only achieves variable-capacity, higher security, higher detection accuracy, and strong recovery ability but also can resist collage attack and mean attack.
Future works include a further increase of the data hiding capacity with the goal of embedding even more information. With that, the quality of the restored content can be increased and additional bits can be used for protection of the data against tampering. For example, using some bits to embed additional temporal information can help counter other attacks, such as frame shuffle.
This work was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and by the University of Brasília (UnB).
RR developed the methods presented in this paper for his master degree dissertation at the University of Brasília (UnB), Brazil. PGF wrote most of the text, figures, and diagrams presented in this manuscript. MCQF is the research adviser and was responsible to guide the research, the writing, and the revising of this manuscript. All authors read and approved the final version of this work.
The authors declare that they have no competing interests.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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