Recently, numerous robust image hashing schemes have been developed for content identification. However, many of these schemes face the challenges of maintaining discrimination while simultaneously resisting large-scale attacks. In this paper, we propose a robust image hashing scheme based on Contrastive Masked Autoencoder with weak-strong augmentation Alignment (CMAA). Leveraging contrastive learning, CMAA is designed to learn features that are robust to large-scale and hybrid attacks while maintaining the discrimination of those features. Specifically, it utilizes distribution divergence to align weak attack augmented features with strong attack augmented features, namely weak-strong augmentation alignment, to enhance the robustness to strong attacks. In addition, a masked vision transformer is incorporated to further enhance content identification performance. CMAA also includes a parameter-free quantization layer to mitigate the loss induced by binarization. Experimental results demonstrate that our method exhibits remarkable robustness against various attacks, including challenging ones such as rotation and hybrid attacks, and delivers excellent identification performance with a F1 score close to 1.0. Our code and supplementary materials are available on Github.

In collaborative system, a large amount of digital image transmission requires a reliable content identification system to ensure the authenticity and copyright of these images. Robust image hashing schemes could efficiently extract robust features of images without manipulating the original image, making them an effective solution for content identification. However, existing schemes face the challenge of resisting complex attacks, such as diverse attack types and their sophisticated combinations, in the real world while maintaining discrimination. The complexity of attacks is reflected in the diversity of attack types, the variation in attack intensity and the combination of multiple attacks. In this study, we propose a robust image hashing approach based on Mixture-of-experts with Hard-sample mining contrastive learning (MiHa). In particular, MiHa utilizes a mixture-of-experts architecture to adaptively extract features from the image under various attacks, including hybrid attacks, thereby enhancing the robustness. Additionally, large-scale attacks can generate hard samples for contrastive learning. To address this, we propose a hard-sample mining contrastive loss that assigns greater weight to these hard samples, thereby further improving the performance of MiHa. Extensive experiments demonstrate that our method achieves superior robustness against various attacks while maintaining competitive discrimination.

Copyright protection of depth image-based rendering (DIBR) videos has raised significant concerns due to their increasing popularity. Zero-watermarking, emerging as a powerful tool to protect the copyright of DIBR 3D videos, mainly relies on traditional feature extraction methods, thus necessitating improvements in robustness against complex geometric attacks and its ability to strike a balance between robustness and distinguishability. This paper presents a novel zero-watermarking scheme based on cross-modality feature fusion within a contrastive learning framework. Our approach integrates complementary information from 2D frames and depth maps using a cross-modality attention feature fusion mechanism to obtain discriminative features. Moreover, our features achieve a better trade-off between robustness and distinguishability by leveraging a designed contrastive learning strategy with an adversarial distortion simulator. Experimental results demonstrate our remarkable performance by reducing the false negative rates to around 0.2% when the false positive rate is equal to 0.5%, which is superior to the state-of-the-art zero-watermarking methods. • Use contrastive learning to balance watermarking robustness and distinguishability. • Employ an adversarial distortion simulator to enhance robustness against various attacks. • Design cross-modality fusion mechanism to achieve better feature representation.

Zero-watermarking is an emerging distortion-free copyright protection method for volumetric medical images. However, achieving both robustness against various malicious attacks and distinguishability between individual images remains challenging. In this article, we propose a novel attack-defending contrastive learning zero-watermarking (ADCL-ZW) scheme to tackle the above challenge using deep learning-based representations. In our approach, we design an attack-defending data enrichment mechanism to enhance the watermarking robustness by generating a large number of image samples under various watermarking attacks. Subsequently, features for both watermarking distinguishability and robustness are enhanced through application of a contrastive loss. In particular, we implement a dual-stream Siamese network architecture to effectively handle both signal attacks and geometric attacks in order to enhance the watermarking performance. Experimental results demonstrate that ADCL-ZW achieves stronger watermarking robustness and a better tradeoff between watermarking robustness and distinguishability compared with state-of-the art zero-watermarking methods. One of the highlighted metrics is that the false-negative rate of ADCL-ZW achieves 0.01 when a fixed false-positive rate is set to 1%, which is more than 13.3 times better than the benchmark methods.