While existing watermarking attack methods can disturb the correct extraction of watermark information, the visual quality of watermarked images will be greatly damaged. Therefore, a concealed attack based on generative adversarial network and perceptual losses for robust watermarking is proposed. First, the watermarked image is utilized as the input of generative networks, and its generating target (i.e. attacked watermarked image) is the original image. Inspired by the U-Net network, the generative networks consist of encoder-decoder architecture with skip connection, which can combine the low-level and high-level information to ensure the imperceptibility of the generated image. Next, to further improve the imperceptibility of the generated image, instead of the loss function based on MSE, a perceptual loss based on feature extraction is introduced. In addition, a discriminative network is also introduced to make the appearance and distribution of generated image similar to those of the original image. The addition of the discriminative network can remove watermark information effectively. Extensive experiments are conducted to verify the feasibility of the proposed concealed attack method. Experimental and analysis results demonstrate that the proposed concealed attack method has better imperceptibility and attack ability in comparison to the existing watermarking attack methods.

Concealed Attack for Robust Watermarking Based on Generative Model and Perceptual Loss

Most of the existing coverless steganography approaches have poor robustness to geometric attacks, because these approaches use features of the entire image to map information, and these features are easy to be lost when being attacked. In order to improve the robustness against geometric attacks, we propose a coverless image steganography method based on multi-object recognition. In this scheme, we firstly use Faster RCNN to detect objects in the image data set, establish a mapping dictionary between object labels and binary sequence. Then we propose a novel mapping rule based on the filtered robust object labels for sequence generation. Therefore, an image can generate robust binary sequence through multi-objects recognition. In the transmission process, the transmitted image has not been modified, so our method can fundamentally resist steganalysis tools and avoid the attacker’s suspicions. In addition, the capacity and hiding rate of the proposed method are both considerable. Evaluations with under geometric attacks shows, on average, $3.1\times $ robustness increase over other five coverless steganography methods. Moreover, evaluations under ten noise attacks shows, on average, the robustness of our method is also excellent, which reaches 83%.

Coverless Image Steganography Based on Multi-Object Recognition

In this work, a novel hybrid algorithm has been developed for achieving imperceptible and robust image steganography for secure data communication. The novelty of the work lies in the careful manipulation of higher frequency coefficient of Discrete Cosine Transform (DCT) to maintain the perceptual quality of the image followed by embedding secret bits in the controlled DCT coefficients using random locations identified by deterministic Coupled Chaotic Map (CCM). The randomness of the CCM map is confirmed by National Institute of Standards and Technology, DIEHARD, ENT and TestU01 test suites. The experimental results demonstrate that the proposed technique has excellent stego-image quality keeping zero Bit Error Rate at maximum embedding capacity (EC). The proposed method has capability to withstand against malicious users as well as outperforms existing steganography techniques in terms of EC, and Peak Signal to Noise Ratio. The security of the proposed technique is subsequently examined by the key length, key sensitivity parameter and histogram analysis.

A hybrid algorithm for robust image steganography

Digital watermarking is an important scientific direction located at the intersection of cybersecurity and multimedia processing. Digital watermarking is used for digital objects copyright protection and protection against forgery. The importance of those tasks is highlighted by the ongoing COVID-19 pandemic that lasts for more than a year and forced multiple industries to transit to online. The main digital watermarking application include active attackers whose goal is to destroy or damage watermarks. Thus, digital watermarking schemes must be robust to attacks. Despite of the large number of existing digital watermarking schemes, a comprehensive analysis of their robustness towards various attacks does not exist. Our study aims to fill this gap. In this article, latest years findings in digital watermarking are systematized. First of all, we review the most prominent distinctive features of watermarking schemes, attack types, and performance measures to facilitate navigation in digital image watermarking. Then, we introduce classification of digital watermarking schemes by their robustness towards various classes of attacks. We believe this classification could be useful for researchers who aim to choose a digital watermarking scheme for their application with a known set of attacks. Finally, we highlight the most promising solutions that further development could lead to schemes with better robustness compared to existing schemes.

Watermarking schemes for digital images: Robustness overview

The excellent cover estimation is very important to the payload location of JPEG image steganography. But it is still hard to exactly estimate the quantized DCT coefficients in cover JPEG image. Therefore, this paper proposes a JPEG image steganography payload location method based on optimal estimation of cover co-frequency sub-image, which estimates the cover JPEG image based on the Markov model of co-frequency sub-image. The proposed method combines the coefficients of the same position in each 8 × 8 block in the JPEG image to obtain 64 co-frequency sub-images and then uses the maximum a posterior (MAP) probability algorithm to find the optimal estimations of cover co-frequency sub-images by the Markov model. Then, the residual of each DCT coefficient is obtained by computing the absolute difference between it and the estimated cover version of it, and the average residual over coefficients in the same position of multiple stego images embedded along the same path is used to estimate the stego position. The experimental results show that the proposed payload location method can significantly improve the locating accuracy of the stego positions in low frequencies.

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JPEG image steganography payload location based on optimal estimation of cover co-frequency sub-image

Considering that traditional image steganography technologies suffer from the potential risk of failure under lossy channels, an enhanced adaptive steganography with multiple robustness against image processing attacks is proposed, while maintaining good detection resistance. First, a robust domain constructing method is proposed utilizing robust element extraction and optimal element modification, which can be applied to both spatial and JPEG images. Then, a robust steganography is proposed based on “Robust Domain Constructing + RS-STC Codes,” combined with cover selection, robust cover extraction, message coding, and embedding with minimized costs. In addition, to provide a theoretical basis for message extraction integrity, the fault tolerance of the proposed algorithm is deduced using error model based on burst errors and decoding damage. Finally, on the basis of parameter discussion about robust domain construction, performance experiments are conducted, and the recommended coding parameters are given for lossy channels with different attacks using the analytic results for fault tolerance. A series of experimental results demonstrate that the proposed algorithm can extract embedded messages with significantly higher accuracy after different attacks, such as compression, noising, scaling and other attacks, compared with the state-of-the-art adaptive steganography, and robust watermarking algorithms, while maintaining good detection resistant performance.

Xiangyang Luo

Papers

Multiple Robustness Enhancements for Image Adaptive Steganography in Lossy Channels