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Images play an important role in transmitting visual information in our life. It could lead to severe consequences if images are manipulated or tampered maliciously. Digital forensics is an important research area to secure multimedia information. Many forensics technologies are applied to protect our community from the abuse of digital information. In many cases, after tampering, attackers could apply operations such as resampling, JPEG compression, blurring, etc. to cover the traces of tampering. Therefore, it is necessary to detect these manipulations in image forensics before exposing forgeries. In this paper, we propose to employ the prediction error filters, ConvNeXt blocks and convolution modules to classify images with different compression quality factors and resampling rates. By tracing the inconsistencies of resampling rates and compression quality factors, it could provide supplementary information for forensics researchers to expose possible forgeries. The proposed method could achieve great classification performance regardless of the interpolation algorithms. Also, it is highly robust against JPEG compression. In addition, the proposed method can be applied for estimating quality factors of JPEG compression.

High-resolution remote sensing images with complex contents have garnered significant attention for practical applications, which also pose substantial challenges related to space occupation, security, and information privacy. Compression is frequently coupled with encryption to balance the above-mentioned issues. Most existing methods for joint compression and encryption are primarily designed for common low-resolution images. Toward this end, we propose a novel compression-and-encryption algorithm for remote sensing images that maintains the JPEG format compatibility while virtually eliminating the block effects in the encrypted images. A novel block segmentation strategy that is friendly to simple images is first introduced, in which the original image is divided into nonoverlapping sub-blocks that are greater than the blocks in JPEG compression. The key that is utilized to encrypt the values of DC coefficients is generated through a fusion of the 2D Logistic-Sine-Coupling Map and SHA-512. After that, a parallel diffusion method is proposed for all nonzero AC coefficients, which significantly enhances the encryption efficiency. The global blocks permutation is conducted to further improve the security. Extensive experiments are carried out to demonstrate that our encryption scheme is highly efficient, provides enhanced security, and maintains format compliance for JPEG. Meanwhile, the presented method can resist various attacks to a certain extent.

Based on wavelet transforms, an algorithm is proposed to scramble digital images. The algorithm changes both the location and the grey grade of image points. As a result, it achieves good scrambling effects and information security. When embedding digital watermarks, the fusion factors are adjusted to maximize fusion intensity using the critical threshold of human eyes for just noticeable difference (JND). Numerical experiments show good information hiding effects and high attack resistance of the hidden images, especially the ability to resist JPEG compression. The proposed algorithm can be used to protect the copyrights of digital images, and network transmission of encrypted hidden information.

A new blind image watermarking algorithm robust against both geometric attack and JPEG compression is proposed. The proposed discrete wavelet transform-fast-Fourier transform(DWT-FFT) composite watermarking scheme embeds a gray image in subband in the DWT domain, and embeds a template in magnitude spectrum in FFT domain. Experimental results have demonstrated that the watermark is robust against both affine transformations and JPEG compression well.