A Digital Watermarking Encryption Technique Based on FPGA Cloud Accelerator

Abstract

Digital watermarking has the properties such as invisibility and anti-aggression, so the digital watermarking technology has been widely used in copyright protection, information hiding. The watermarking technology takes into account the invisibility and robustness of the watermark by controlling the embedding intensity and position of the watermark mainly in the transformation domain. In this paper, discrete cosine transform (DCT) is adopted to transform the given image from spatial domain to frequency domain for adding watermark information. In order to meet the demands of image watermarking batch processing and cloud processing in the future, this paper optimized the DCT algorithm and the data precision, and successfully deployed the designed accelerator kernel on the FPGA cloud platform to speed up the processing of watermarking. The implementation of data processing based on cloud platform is the development trend of big data era. The cloud platform adopted in this paper is based on the OpenCL heterogeneous architecture combining CPU and FPGA. The cloud-based implementation makes digital watermarking application highly extensible, widely shareable, and more secure. The whole system implements a series of complete cloud processes including image decoding, image preprocessing, watermark embedding, and watermarked image encoding. The watermarking algorithm is accelerated by the efficient parallel computing capabilities of FPGA. It can be seen that the result of acceleration is remarkable, providing the state-of-the-art throughput of 1.676 GBps and the highest processing speed of 937 FPS for 800 × 800 sized colorimage. INDEX TERMS Cloud services, parallel operation, integer DCT, data encryption and watermarking, heterogeneous architecture.

A Hidden DCT-Based Invisible Watermarking Method for Low-Cost Hardware Implementations

“…It consumes 4582 Les, which are the logic equivalent of 4582 LUTs and 4582 single-bit registers. Compared with other works, our design has a notable advantage in saving logic resources, except for [29,36]. However, [29] showed relatively low throughput (11.8 im./sec), whereas our design is capable of reaching the throughput of 2.57 × 10 4 fps.…”

“…According to (38) Although our design consumes more resources than [29], its throughput and throughput per Watt are both three orders of magnitude higher. Compared with [36], our design consumes less LUTs but more flip flops. However, [36] uses four more DSP48E resources than our design.…”

An Improved Hybrid DCT-DWT Blind Watermarking Technique for Securing Multimedia Images

“…Digital image watermarking embeds a secret image into a carrier image for transmit of the secret image to a intended receiver [10]. The extraction process for any unintended receiver is made difficult or impossible.…”

“…Discrete Cosine Transform (DCT) is defined as an orthogonal transformation of image processing and signal processing. DCT provides high-computational complexity, low-error rate and high-compression ratio [13]. The compression performance of Discrete Cosine Transform on JPEG images is comparatively high and transforms the signal from one domain to another domain i.e., Spatial to frequency.…”

Robust Secure Color Image Watermarking Using 4D Hyperchaotic System, DWT, HbD, and SVD Based on Improved FOA Algorithm

“…Our NC (ref NC) No attack 1.0 (1.0 [25], 1.0 [38]) Gaussian low-pass filter 0.827244 Median filtering (5,1) 0.8874 (0.9968 [25], 0.5743 [39], 0.9356 [40], 0.8814 [38], 0.8370 [41]) Median filtering (3,3) 0.847447 (0.7897 [42], 0.9188 [30], 0.9258 [9]) Average filter' (3,3) 0.826939 (0.7569 [42]) Gaussian noise (M � 0, V � 0.0001) 0.94806 (0.9706 [25], 0.9256 [39], 0.9387 [40], 0.9131 [38]) Salt and pepper noise (0.001) 0.984562 (0.9952 [25], 0.9287 [39], 0.9122 [40], 0.9902 [38], 0.9421 [43]) Speckle noise 0.951758 JPEG compression (Qf � 30) 0.87903 (0.9968 [25], 0.8594 [39], 0.9789 [40], 0.8469 [38]) JPEG compression (Qf � 90) 0.92956 (0.9862 [30], 0.9061 [43], 0.89109 [32]) Sharpening attack (0.2) 0.96612 (0.9138 [42], 0.9579 [9]) Sharpening attack (0.8) 0.856457 Sharpening attack (1.0) 0.8052 (0.9638 [25], 0.9877 [39], 0.9366 [40], 0.9999 [38]) Histogram equalization 0.8785 (0.8805 [11]) Motion blur 0.830194 Flip (horizontal/Vertical) 0.9912 (1.0 [19])…”