With the widespread use of smartphones and the rise of intelligent software, we can manipulate captured photos anytime and anywhere, so the fake photos finally obtained look “Real.” If these intelligent operation methods are maliciously applied to our daily life, then fake news, fake photos, rumors, slander, fraud, threats, and other information security issues around us can happen all the time. Today’s intelligent retouching software can make various modifications to photos, some of which do not change the content that the photos themselves want to express, such as retouching, contrast improvement, and so on. In this article, we mainly study the three operation modes of changing the authenticity of photo contents, which are Copy-move, Splicing, and Removal. Few scholars have done relevant research due to the lack of a corresponding dataset. To address this issue, we elaborately collect a novel dataset, called the multi-realistic scene manipulation dataset ( MSM30K ), which consists of 30,000 images, including three types of tampering methods, and covering 32 different tampering scenes in life. In addition, we propose a unified detection network: the efficient search and recognition network ( ESRNet ) for three tampering methods. It mainly includes four main modules: Efficient feature pyramid network ( EFPN ), Residual receptive field block with attention ( RFBA ), Hierarchical decoding identification ( HDI ), and Cascaded group-reversal attention ( GRA ) blocks. On these three datasets, ESRNet can reach 0.81 on the S-measure, 0.72 on the F-measure, and 0.85 on the E-measure. The inference speed is ~53 fps on a single GPU without I/O time. ESRNet outperforms various state-of-the-art manipulation detection baselines on three image manipulation datasets.

Double random-phase encoding- (DRPE-) based compressive sensing (CS) systems support image authentication for noisy images. When extending such systems to resource-constrained applications, how to ensure the authentication strength for noisy images becomes challenging. To tackle the issue, an efficient and secure image authentication scheme is presented. The phase information of the plain image is generated using DRPE and quantized into a binary image as the authentication information. Meanwhile, a sparser error matrix generated by the same plain image and vector quantization (VQ) image works as the input of CS. The authentication information and VQ indexes are self-hidden into the quantized measurements to construct the combined image. Then, it is permutated and diffused with the chaotic sequences generated from a modified Henon map. After decryption at the receiver side, the verifier can implement the blind authentication between the noisy decoded image and the reconstructed image. Supported by the detailed numerical simulations and theoretical analyses, the DRPE-CSVQ exhibits more powerful compression and authentication capability than its counterpart.

A novel method of meaningful image encryption is proposed in this paper. A secret image is encrypted into another meaningful image using the algorithm of reversible data hiding (RDH). High covertness can be ensured during the communication, and the possibility of being attacked of the secret image would be reduced to a very low level. The key innovation of the proposed method is that RDH is applied to compressive sensing (CS) domain, which brings a variety of benefits in terms of image sampling, communication and security. The secret image after preliminary encryption is embedded into the sparse representation coefficients of the host image with the help of the dictionary. The embedding rate could reach 2 bpp, which is significantly higher than those of other state-of-art schemes. In addition, the computational complexity of receiver is reduced. Simulations verify our proposal.

The first joint fingerprinting and decryption (JFD) for vector quantization (VQ) images addressed the problem that the decrypted multimedia data may be redistributed from authorized customers to unauthorized customers. The scheme also caused conventional JFD methods to be equipped with a special ability to resist noise interference. Till now, some existing schemes related have been proposed to protect the multimedia content and distribution, but these schemes failed to tackle several problems existing in the original JFD scheme based on VQ image, including high transmission cost and severe fingerprinted image distortion. In this paper, we propose a novel JFD method by combining a weight-sum function with fingerprinting embedding and extraction for VQ images. Under the combination, the visual quality of the fingerprinted image is further improved; also the fingerprint extraction implements a blind extraction process. Experiments and analyses demonstrate the feasibility of the proposed method.