Connectivity-Based Watermarking Robust to Geometrical Attacks

A novel robust watermarking scheme based on image connectivity is proposed. In the scheme, the connected objects are obtained according to selected connectivity pattern, and the gravity centers are calculated in several bigger objects as the reference points for watermark embedding. Based on these reference points and the center of the whole image, several sectors are formed, and the same version of watermarks is embedded into these sectors and their opposites. Thanks to the very stable gravity center of the connected objects, watermark detection is synchronized successfully. Simulation results show that the watermark can survive under both local and global geometrical distortions.

Geometric attack resistant image watermarking based on MSER

Geometric distortions are simple and effective at- tacks rendering many watermarking methods useless. They make detection and extraction of the embedded watermark difficult or even impossible by destroying the synchroniza- tion between the watermark reader and the embedded water- mark. In this paper, we propose a blind content-based image watermarking scheme against geometric distortions. Firstly, the MSER detector is adopted to extract a set of maximally stable extremal regions which are affine covariant and robust to geometric distortions and common signal processing. Sec- ondly, every original MSER is fitted into an elliptical region that was proved to be affine invariant. In order to achieve rota- tion invariance, an image normalization process is performed to transform the elliptical regions into circular ones. Finally, watermarks are repeatedly embedded into every circular disk by modifying the wavelet transform coefficients. Experimen- tal results on standard benchmark demonstrate that the pro- posed scheme is robust to geometric distortions as well as common signal processing.

Intensifying uneven charge distribution via geometric distortion engineering in atomically dispersed M-N_(x)/S sites for efficient oxygen electroreduction

Fine regulation of geometric structures has great promise to acquire specific electronic structures and improve the catalytic performance of single-atom catalysts,yet it remains a challenge.Herein,a novel seed encapsulation–decomposition strategy is proposed for the geometric distortion engineering and thermal atomization of a series of Cu-N_(x)/S moieties anchored on carbon supports.During pyrolysis,seeds(Cu^(2+),CuO,or Cu_(7)S_(4) nanoparticles)confined in metal organic framework can accommodate single Cu atoms with Cu–N or Cu–S coordination bonds and simultaneously induce C–S or C–N bond cleavage in the second coordination shell of Cu centers,which are identified to manipulate the distortion degree of Cu-N_(x)/S moieties.The severely distorted Cu-N3S molecular structure endows the resultant catalyst with excellent oxygen reduction reaction activity(E_(1/2)=0.885 V)and zinc-air battery performance(peak power density of 210 mW·cm^(−2)),outperforming the asymmetrical and symmetrical Cu-N4 structures.A combined experimental and theoretical study reveals that the geometric distortion of Cu-N_(x)/S moieties creates uneven charge distribution by a unique topological correlation effect,which increases the metal charge and shifts the d-band center toward the Fermi level,thereby optimizing the inter-mediate adsorption energy.

Robust blind image watermarking based on interest points

Digital watermarking technology plays an essential role in the work of anti-counterfeiting and traceability.However,image watermarking algorithms are weak against hybrid attacks,especially geometric attacks,such as cropping attacks,rotation attacks,etc.We propose a robust blind image watermarking algorithm that combines stable interest points and deep learning networks to improve the robustness of the watermarking algorithm further.First,to extract more sparse and stable interest points,we use the Superpoint algorithm for generation and design two steps to perform the screening procedure.We first keep the points with the highest possibility in a given region to ensure the sparsity of the points and then filter the robust interest points by hybrid attacks to ensure high stability.The message is embedded in sub-blocks centered on stable interest points using a deep learning-based framework.Different kinds of attacks and simulated noise are added to the adversarial training to guarantee the robustness of embedded blocks.We use the ConvNext network for watermark extraction and determine the division threshold based on the decoded values of the unembedded sub-blocks.Through extensive experimental results,we demonstrate that our proposed algorithm can improve the accuracy of the network in extracting information while ensuring high invisibility between the embedded image and the original cover image.Comparison with previous SOTA work reveals that our algorithm can achieve better visual and numerical results on hybrid and geometric attacks.

Geometrically invariant color image watermarking scheme using feature points

Geometric distortion is known as one of the most difficult attacks to resist. Geometric distortion desynchronizes the location of the watermark and hence causes incorrect watermark detection. In this paper, we propose a geometrically invariant digital watermarking method for color images. In order to synchronize the location for watermark insertion and detection, we use a multi-scale Harris-Laplace detector, by which feature points of a color image can be extracted that are invariant to geometric distortions. Then, the self-adaptive local image region （LIR） detection based on the feature scale theory was considered for watermarking. At each local image region, the watermark is embedded after image normalization. By binding digital watermark with invariant image regions, resilience against geometric distortion can be readily obtained. Our method belongs to the category of blind watermarking techniques, because we do not need the original image during detection. Experimental results show that the proposed color image watermarking is not only invisible and robust against common signal processing such as sharpening, noise adding, and JPEG compression, but also robust against the geometric distortions such as rotation, translation, scaling, row or column removal, shearing, and local random bend.