Objective

2

The copyright protection has been the hotspot with the amount of digital documents increased dramatically. In order to protect the document copyright and locate the source of the leaked document

watermarking technology innovation for documents has been widely focused on. The protection can be realized via adding invisible digital watermark information (e.g.

device number

date

etc.) to the document. To realize the traceability of document leakage

the leaked source can be located by extracting the watermark from the document once the watermarked document is leaked. Meanwhile

the current watermarking technology can also act as a deterrent which effectively reduce the occurrence of the leaking events. The current document watermarking methods can be divided into five categories: document structure based methods

natural language processing based methods

grid pattern based methods

image based methods and font based methods. Among them

the font based methods guarantee the best performance in the view of robustness and transparency. The main idea of such methods is representing the watermark information into the characteristics of the fonts (e.g.

the size

shape or brightness) while the modified fonts maintain the high visual consistency with the original one. The robustness

transparency

capacity as well as the integrity can be achieved simultaneously. However

the existing font based methods need to design the modification features manually

and cannot automatically generate the new fonts. For the Chinese character system which contains a large number of characters

such methods will cause a labor cost workload and severely less efficiency. To overcome such drawbacks

this research proposes an automatic font generation based robust document watermarking scheme.

Method

2

The framework of such scheme is comprised of an end-to-end encoder-decoder structure automatic font generation network

a character selection embedder and a neural network based extractor. With the designed font generation network

the deformed font library is further utilized for embedding the watermark generated automatically. Meanwhile

a differentiable noise layer is complemented between the encoder and the decoder to simulate the distortion process in order to realize the robustness against different distortions

so that the encoder can learn better features to create the new font and the decoder can be trained to be adaptive to such distortions. This research designs a combined noise layer that can effectively simulate the common distortions via the common distortions in digital transmission channels (e.g.

screenshots

scaling

Gaussian noise and JPEG compression). The whole font generation network consists of four parts: encoder

noise layer

decoder and adversarial recognizer. The encoder receives the watermark information and the carrier character image to generate the encoded character image. The noise layer adds noise to the encoded image to generate the noisy image. In particular

several of six simulated noise layers (identity mapping layer

scaling layer

translation layer

rotation layer

Gaussian noise layer and Gaussian blur layer) are randomly opted as a combined noise layer at each iteraction. The decoder receives the noisy image and outputs the corresponding watermark label. The adversarial recognizer tries to detect whether the current image is a carrier character image or an encoded one

which aids to improve the visual quality of the generated font. The encoder provides training samples for the extractor to ensure better extraction performance

and the extractor guides the generation direction of encoder to create better character images. The two coordinated modules make the generated font with higher visual quality and stronger robustness. Based on the well trained font generation

the network has generated the watermarked font library via feeding them with an original font library and different watermark signal. Each character in the font library can corresponds to different perturbation

which can be decoded to different watermark signal further. Hence

based on the generated font library

the corresponding character in the codebook is sorted out in the watermark embedding stage according to the current watermark information to replace the current character in the input document. The watermark information can be embedded into the whole original document and generate the watermarked document in this way. In the extraction stage

the whole document is firstly divided into some single character images by character segmentation after receiving the distorted watermarked document transmitted with digital channel. Each character is sent to the pre-trained decoder in the watermarking generation stage. The watermark information embedded in the current character can be accurately extracted. The characters in the document undergo the transformation distortion from digital signal to analog signal (A-D) process and from analog signal to digital signal (D-A) process

and the image quality is greatly reduced in the context of the print-scan scene. Simultaneously

such process that contained various image attacks cannot be accurately simulated by differentiable distortions

so the robustness against print-scanning distortions should be considered as a target. To achieve such robustness

this proposal is a fine-tuning scheme for the extractor which can effectively train the extractor to be adaptive to the print-scanning distortions. Specifically

the font generation model is fixed as a pre-training network and a set of following documents are embedded with watermarks based on the pre-trained embedder. The real print-scanning process is conducted on such documents to generate the distorted image library. Based on the distorted image and its watermark

the decoder is fine-tuned to be adaptive to the distorted features further. The robustness against print-scanning distortion can be achieved.

Result

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This scheme embeds 252 bits of watermark into a real document containing 252 Chinese characters in comparison of the visual quality and robustness with other document watermarking methods. The results show that the peak signal to noise ratio(PSNR)

structural similarity(SSIM) and subjective quality scores of the proposed scheme are higher with 11.68 dB

0.08 and 5.8 % respectively

demonstrating the qualified visual quality of the proposed scheme. For the robustness

the watermark extraction rate of the scheme is 100 % under the screenshot and scaling

and the performance under JPEG compression and Gaussian noise is approximately equivalent to that of other methods. For the print-scan scene

the watermark extraction rates of the scheme in the font size of three

four

small four and five are realized 2.4 %

3.07 %

1.34 % and 0.02 %

respectively. The qualified performance is achieved under different mismatched printing and scanning qualities as well

which indicates that the scheme has higher robustness in resisting the print-scanning distortions.

Conclusion

2

Compared with the existing Chinese character watermarking methods based on the manually designed font library

the proposed scheme can automatically generate the tagged Chinese font library that is similar to the target font visually

which effectively reduces the complexity of the font generation. The experimental results show that the proposed document watermarking scheme has presented better visual quality and embedding capacity. In addition

the proposed scheme maintains the strong robustness against digital editing channel as well as the print-scanning channel. However

the scheme is not suitable yet for print-shooting and screen-shooting process at present. Future research will be concentrated on how to design robust document watermarking schemes for these two scenes.