Medical images play a crucial role in doctors' clinical diagnosis and treatment. However, the transmission and sharing of such private information raises security concerns. To address this issue, image hiding is used as an effective technique to protect images. To achieve large hiding capacity, lossless recovery and anti-steganalysis, we propose a novel two-stage medical image-hiding method in this paper. In the first stage, a QR code for the patient diagnosis information (PDI) is generated and embedded into a secret medical image using reversible data hiding. In the second stage, the secret medical image containing PDI is hidden in a natural target image. A kind of lossless compression technique named soft compression is innovatively introduced in two hiding stages, to ensure that the reconstructed secret medical image and PDI are exactly identical to the original ones. Moreover, an adaptiven-LSB model is proposed to improve the stego image quality. Extensive experimental results show that our method achieves a PSNR of over 40dB for the stego image at 2 BPP while recovering the PDI and secret medical image with 100% accuracy on the DIV2K, COCO and ImageNet datasets. It outperforms other state-of-the-art methods in terms of hiding invisibility, recovery accuracy and security.

The goal of multiple-image hiding is to hide several secret images within another carrier image without significantly changing its appearance, and then perfectly reconstruct all of the secret images. The challenge is to ensure that the stego-image has great visual quality and can resist various steganalysis under the premise of hiding as much information as possible in one image. To address this issue, the majority of known image-hiding methods focus on hiding images using compression techniques. In this article, we present a novel multiple-image hiding method based on up-sampling and reversible color transformation. First, the interpolation algorithm up-samples the carrier image, so that the attribute of similar neighboring pixel values in the up-sampled image can significantly improve the effect of image hiding. The embedding procedure is then performed using the proposed Euclidean Distance (ED)-based block matching and reversible color transformation, which decreases the chance of local blurring in the stego-image. Experimental results show that the proposed method surpasses existing advanced methods by achieving an average of 33 dB and 28 dB of PSNR for the stego-image with a hiding capacity 2 BPP and 8 BPP, and obtaining 100% reconstructing accuracy for all secret images. It also has a high level of resistance to steganalysis and a strong robustness against various image-processing attacks.

In the field of image steganography, quality, security, and capacity emerge as three crucial aspects for ensuring the security of images stored on cloud servers. However, many existing methods fail to strike a balance on these three aspects according to the various requirements of image users. To solve this issue, we propose an Adaptable Image Steganography Model (AISM) capable of customizing suitable steganography strategies for different user requirements. Initially, AISM customizes appropriate down-sampling methods, ratios, and up-sampling ratios for secret/cover images based on requirements, followed by the image sampling process. Following that, an embedding algorithm based on pixel-value coding is proposed, which maps pixel values from [0, 255] to [-9, 9] and then replaces the high-frequency sub-band coefficients of the up-sampled image. During the embedding process, no auxiliary information is generated, which is a key aspect for user-friendliness. Extensive experimental results demonstrate that our method is capable of customizing satisfactory steganography strategies for various user requirements. Moreover, our method outperforms many state-of-the-art methods in terms of quality and security, i.e., lossless recovery of secret images, PSNR of 40 dB for stego-images, evasion from detection by six classic steganalysis tools.

Abstract Dense video captioning aims to locate multiple events in an untrimmed video and generate captions for each event. Previous methods experienced difficulties in establishing the multimodal feature relationship between frames and captions, resulting in low accuracy of the generated captions. To address this problem, a novel Dense Video Captioning Model Based on Local Attention (DVCL) is proposed. DVCL employs a 2D temporal differential CNN to extract video features, followed by feature encoding using a deformable transformer that establishes the global feature dependence of the input sequence. Then DIoU and TIoU are incorporated into the event proposal match algorithm and evaluation algorithm during training, to yield more accurate event proposals and hence increase the quality of the captions. Furthermore, an LSTM based on local attention is designed to generate captions, enabling each word in the captions to correspond to the relevant frame. Extensive experimental results demonstrate the effectiveness of DVCL. On the ActivityNet Captions dataset, DVCL performs significantly better than other baselines, with improvements of 5.6%, 8.2%, and 15.8% over the best baseline in BLEU4, METEOR, and CIDEr, respectively.