The first joint fingerprinting and decryption (JFD) for vector quantization (VQ) images addressed the problem that the decrypted multimedia data may be redistributed from authorized customers to unauthorized customers. The scheme also caused conventional JFD methods to be equipped with a special ability to resist noise interference. Till now, some existing schemes related have been proposed to protect the multimedia content and distribution, but these schemes failed to tackle several problems existing in the original JFD scheme based on VQ image, including high transmission cost and severe fingerprinted image distortion. In this paper, we propose a novel JFD method by combining a weight-sum function with fingerprinting embedding and extraction for VQ images. Under the combination, the visual quality of the fingerprinted image is further improved; also the fingerprint extraction implements a blind extraction process. Experiments and analyses demonstrate the feasibility of the proposed method.
The digitized form of numerous multimedia contents not only facilities various operations but also allows fast distribution from one physical location to another via the Internet. As any advancement in technique can always be used in good and bad ways, the convenience of technique simultaneously incurs the potential security hazard when illegally redistributing the protected content via the Internet. To improve the security of multimedia contents in transmission and distribution, digital fingerprint has been widely investigated over the past few years [
Specifically, fingerprint embedded into host image is especially designed in various application scenarios. In the transmitter-side fingerprinting application [
The first receiver-side fingerprinting method [
In this paper, we improve the method of decryption and fingerprinting process of [ The encrypted codeword indices are capable of being efficiently transmitted with much smaller bandwidth and cost. The flexibility of fingerprint embedding can be ensured, and the changeable maximum fingerprint capacity can be achieved. The fingerprint extraction operation in the traitor tracing process is a blind extraction process. Comparing with the original scheme in [
A brief overview of the concepts used in the proposed scheme is discussed as follows.
Vector quantization (VQ) is the technique that converts original image blocks into specific codeword indices relying on the known codebook to implement the goal of compression, in which the design of codebook directly affects the compression efficiency and image restoration quality. It has a similar functionality with palette technique, the latter transforms color value into index value which can be found by color look-up table, and the maximum of indispensable color of an image directly determines the ultimate compression result. Generally, vector quantization (VQ) designed for image compression is composed of two parts: encoding and decoding processes. Encoding process is to divide original image into nonoverlapping blocks with size of
As [
An essential starting point of designing weight-sum function is to conceal as much additional data as possible into cover image with modifying as little data as possible, and its fundamental objective is consistent with that of exploiting modification direction (EMD) [
The workflow of the weight-sum function.
Note that embedding (
The framework of the proposed JFD scheme based on VQ is shown in Figure
The framework of the proposed JFD scheme.
In this subsection, the proposed JFD scheme in the receiver side is presented in detail. We replace the codeword-index-based JFD process with the proposed block-based JFD method. That is, one codeword index is only regarded as a part of the block. The process of the proposed JFD scheme is illustrated in Figure
The whole process of block-based JFD scheme.
Exploiting the block-based idea is mainly attributed to two advantages: one is that only modifying one codeword index with
Note that the proposed image encryption process based on dynamic-key trees depending on session key is different from the original encryption scheme. Consider the same situation where both the original scheme and the proposed JFD scheme accomplish encryption process with dynamic-key trees. The original scheme in [
Similarly, suppose both encrypted VQ indices and
Obtain the encrypted indices together with the codebook size
Set all divided blocks as unprocessed encrypted index-based blocks.
Generate the fingerprint subsequence
If (
If
Check whether there exist unprocessed encrypted index-based blocks. If they exist, set the current block as processed block, and go to Step 4.
When illegally redistributing the fingerprinted decryption content via the Internet, the effective way to find the traitor is to examine the digital content with proper fingerprint extraction algorithm. It should be interpreted that since the inverse permutation process mentioned in Figure
The flow diagram of fingerprint extraction process.
The simple extraction steps are depicted as follows.
Divide the fingerprinted and decrypted image into
Compute the binary value of fingerprinting subsequence from each block with weight-sum function.
Reconstruct the extracted fingerprinting subsequence in turn to generate a user’s fingerprint sequence.
Since the fingerprinting extraction method in the proposed JFD scheme is implemented in the receiver side, i.e., one piece of multimedia data may have many various versions to stand for different users’ identities, the correlation between the extracted fingerprint
In this section, we offer experimental results to confirm the effectiveness of the improved JFD scheme.
Similarly, six standard gray-scale images sized 512 × 512 pixels, including Lena, Plane, Baboon, Boat, Pepper, and Barbara images, are tested with a series of experiments. The VQ images trained by standard gray-scale images are treated as the original images shown in Figure
Six
As previously mentioned, our proposed encryption method is to encrypt image contents into codeword indices to improve transmission efficiency in the transmitter side. When the encrypted indices are intercepted, even if the codebook opened can decode the encrypted indices into image contents, the hacker still cannot know any useful information related to the original image, the property of which implies the security of encryption is desirable. In this article, the size of codebook is set as 256 and the decoded encrypted indices are shown in Figure
Encrypted results under both permutation and dynamic-key trees. (a) Encrypted Lena image (PSNR= 9.57 dB), (b) encrypted Plane image (PSNR=8.68 dB), (c) encrypted Baboon image (PSNR= 9.85 dB), (d) encrypted Boat image (PSNR= 8.97 dB), (e) encrypted Pepper image (PSNR= 8.90 dB), and (f) encrypted Barbara image (PSNR=8.83 dB).
In a joint fingerprinting and decryption process, a receiver is supposed to require both dynamic-key and fingerprint sequence and then divide the obtained indices into blocks and embed fingerprint subsequence into each block during the decryption process. In other words, when completing one block decryption, a given fingerprint subsequence is simultaneously embedded into the decrypted block. Figure
Fingerprinted decryption images using different L Codebooks. (a) Fingerprinted Lena image (L=256, PSNR=30.06dB), (b) fingerprinted Lena image (L=512, PSNR=31.23dB), (c) fingerprinted Lena image (L=1024, PSNR=32.17dB), (d) fingerprinted Plane image (L=256, PSNR=29.69dB), (e) fingerprinted Plane image (L=512, PSNR=30.43dB), (f) fingerprinted Plane image (L=1024, PSNR=31.16dB), (g) fingerprinted Baboon image (L=256, PSNR=23.91dB), (h) fingerprinted Baboon image (L=512, PSNR=24.26dB), (i) fingerprinted Baboon image (L=1024, PSNR=24.57dB), (j) fingerprinted Boat image (L=256, PSNR=28.41dB), (k) fingerprinted Boat image (L=512, PSNR=39.13dB), (l) fingerprinted Boat image (L=1024, PSNR=30.05dB), (m) fingerprinted Pepper image (L=256, PSNR=29.63dB), (n) fingerprinted Pepper image (L=512, PSNR=30.24dB), (o) fingerprinted Pepper image (L=1024, PSNR=31.15dB), (p) fingerprinted Barbara image (L=256, PSNR=25.12dB), (q) fingerprinted Barbara image (L=512, PSNR=25.67dB), and (r) fingerprinted Barbara image (L=1024, PSNR=26.21dB).
In this section, the relationship between the number of modification blocks and the embedding performance is further analyzed to present that the size of block has great influence on the embedding performance and the modifiable block number. It should be emphasized that, in this article, two dividing processes are mentioned, namely, pixel-based blocks for VQ process and index-based blocks for JFD process. For VQ process, as mentioned before, the original image is divided into 4 × 4-dimension vector, and thus a 512 × 512-pixel image contains 16384 pixel-based blocks. As for JFD process, the divided pixel-based blocks in VQ process had been substituted with code indices, and the code indices needed to further divide into index-based blocks to be embedded fingerprint. For instance, when containing one index in an index-based block, the maximum embeddable capacity of fingerprint sequence is 16384 bits for a 512 × 512-pixel image. Here, the block mentioned in this section refers to the index-based blocks, and the relationship of the modified block number under different block sizes and embedding rate is presented in Figure
Modified block number versus embedding rate under different block size.
In terms of the security of encryption, it can be classified into two processes: one is that a given original image is first converted into index matrix under the VQ process and then a permutation process is performed on the generated index matrix to enhance the security during transmission, and the other is that the dynamic-key trees based on session key are exploited in the code substitution process, since the session key only exists in a limited time and the security performance is further enhanced. Therefore, an attacker that intercepts the encrypted indices from the Internet cannot obtain any useful information related to the original encrypted indices.
In this section, the comparison results with the proposed method and the scheme [
The main task in the sender side is to accomplish the encryption for VQ images. In the original scheme, the VQ images are encrypted with codeword substitution, which is based on codeword contents and then transmitted into the receiver side. On the contrary, for the proposed encryption scheme, a given codebook is first sorted and then the codeword indices in the sorted codebook are employed in the encrypted process; i.e., the proposed encryption process is to substitute the current block in the original image with any index depending on dynamic-key trees in the sorted codebook. In this way, the encryption result is to generate encrypted indices which is different from that in the original scheme. Obviously, with the proposed encryption method, the bandwidth and cost for transmission process are decreased significantly compared with the original scheme.
Once illegal multimedia contents, such as illegal image-based contents, occurred via the Internet, one can easily know the traitor by extracting the embedded fingerprint sequence. For the original scheme, the extraction of a fingerprint sequence is accomplished with the guidance of the original VQ image. Conversely, the extraction of fingerprint sequence in our method is a blind extraction process. Specifically, the fingerprinted image is first divided into blocks with 2
Although the original JFD method based on VQ can embed fingerprint into each block during the decryption process, there exists a major concern; i.e., the embedding capacity is constant. For instance, when the block size is set as 4 × 4, a 512 × 512-pixel image contains 16384 blocks, and thus the embedding capacity of fingerprint is 16384 bits. This is quite inflexible and may limit the number of users due to the fact that various users have different fingerprint lengths. However, this concern can be effectively resolved by index-based fingerprint embedding because it offers a variety of options for fingerprint embedding. Thus, the proposed method is more applicable for practical scenarios.
In this module, PSNRs of fingerprinted decryption images under the embedding rate 0.75 bpp (bits per pixel) will be further compared with the original JFD scheme [
PSNRs under various L with method in [
PSNRs (dB) | |||||||
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Size | images | Lena | Plane | Baboon | Boat | Pepper | Barbara |
L=256 | Fingerprinted [ | 29.96 | 29.23 | 23.73 | 27.95 | 29.17 | 24.82 |
Fingerprinted (Proposed) | 30.60 | 29.69 | 23.91 | 28.41 | 29.63 | 25.12 | |
Original | 31.36 | 30.57 | 24.37 | 29.38 | 30.72 | 25.80 | |
| |||||||
L=512 | Fingerprinted [ | 30.81 | 30.11 | 23.99 | 28.78 | 29.93 | 25.31 |
Fingerprinted (Proposed) | 31.23 | 30.43 | 24.26 | 29.13 | 30.24 | 25.67 | |
Original | 32.24 | 31.57 | 24.70 | 30.15 | 31.40 | 26.38 | |
| |||||||
L=1024 | Fingerprinted [ | 31.67 | 30.72 | 24.32 | 29.55 | 30.83 | 25.97 |
Fingerprinted (Proposed) | 32.17 | 31.16 | 24.57 | 30.05 | 31.15 | 26.21 | |
Original | 33.20 | 32.23 | 25.03 | 30.88 | 32.13 | 27.05 |
The functionalities of the proposed method are further discussed and compared with mechanisms [
Comparisons of the functionalities of our method and related mechanisms.
Functionality | Blind extraction | Transmission cost | The flexibility of maximum capacity of fingerprint |
---|---|---|---|
[ | Yes | Higher | Yes |
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[ | No | Higher | Yes |
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[ | No | Higher | Yes |
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[ | Yes | Higher | No |
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Our scheme | Yes | lower | Yes |
This paper proposes a block-based improved JFD scheme with the help of the method in [
All data generated or analyzed during this study are included in this article.
An earlier version of this paper was presented at the National Information Hiding Workshop (CIHW2018,
The authors declare that they have no conflicts of interest.
This work was supported by the National Natural Science Foundation of China (Grant nos. 61602158, 61572089, U1604156, and 61772176), the Science Foundation for the Excellent Youth Scholars of Henan Normal University (Grant no. YQ201607), the Production and Learning Cooperation and Cooperative Education Project of Ministry of Education of China (Grant no. 201702115008), the Fundamental Research Funds for the Central Universities (Grant nos. 106112017CDJQJ188830, 106112017CDJXY180005), the Chongqing Research Program of Basic Research and Frontier Technology (Grant nos. cstc2017jcyjBX0008, cstc2014jcyjA40030), Science and Technology Research Project of Henan Province (Grant nos. 182102210374, 172102210045, and 182102210362), the Natural Science Foundation of Henan province (Grant no. 182300410368), the Plan for Scientific Innovation Talent of Henan Province (Grant no. 184100510003), and the Young Scholar Program of Henan Province (Grant no. 2017GGJS041).