Representing Noisy Image Without Denoising.

A long-standing topic in artificial intelligence is the effective recognition of patterns from noisy images. In this regard, the recent data-driven paradigm considers 1) improving the representation robustness by adding noisy samples in training phase (i.e., data augmentation) or 2) pre-processing the noisy image by learning to solve the inverse problem (i.e., image denoising). However, such methods generally exhibit inefficient process and unstable result, limiting their practical applications. In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing. Here, the noise-robust representation is designed as Fractional-order Moments in Radon space (FMR), with also beneficial properties of orthogonality and rotation invariance. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases, and the introduced fractional-order parameter offers time-frequency analysis capability that is not available in classical methods. Formally, both implicit and explicit paths for constructing the FMR are discussed in detail. Extensive simulation experiments and robust visual applications are provided to demonstrate the uniqueness and usefulness of our FMR, especially for noise robustness, rotation invariance, and time-frequency discriminability.

A Principled Design of Image Representation: Towards Forensic Tasks.

Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society. Like other vision-related applications, forensic analysis relies heavily on the proper image representation. Despite the importance, current theoretical understanding for such representation remains limited, with varying degrees of neglect for its key role. For this gap, we attempt to investigate the forensic-oriented image representation as a distinct problem, from the perspectives of theory, implementation, and application. Our work starts from the abstraction of basic principles that the representation for forensics should satisfy, especially revealing the criticality of robustness, interpretability, and coverage. At the theoretical level, we propose a new representation framework for forensics, called dense invariant representation (DIR), which is characterized by stable description with mathematical guarantees. At the implementation level, the discrete calculation problems of DIR are discussed, and the corresponding accurate and fast solutions are designed with generic nature and constant complexity. We demonstrate the above arguments on the dense-domain pattern detection and matching experiments, providing comparison results with state-of-the-art descriptors. Also, at the application level, the proposed DIR is initially explored in passive and active forensics, namely copy-move forgery detection and perceptual hashing, exhibiting the benefits in fulfilling the requirements of such forensic tasks.

PS-Net: A Learning Strategy for Accurately Exposing the Professional Photoshop Inpainting.

Restoring missing areas without leaving visible traces has become a trivial task with Photoshop inpainting tools. However, such tools have potentially illegal or unethical uses, such as removing specific objects in images to deceive the public. Despite the emergence of many forensics methods of image inpainting, their detection ability is still insufficient when attending to professional Photoshop inpainting. Motivated by this, we propose a novel method termed primary-secondary network (PS-Net) to localize the Photoshop inpainted regions in images. To the best of our knowledge, this is the first forensic method devoted specifically to Photoshop inpainting. The PS-Net is designed to deal with the problems of delicate and professional inpainted images. It consists of two subnetworks: the primary network (P-Net) and the secondary network (S-Net). The P-Net aims at mining the frequency clues of subtle inpainting features through the convolutional network and further identifying the tampered region. The S-Net enables the model to mitigate compression and noise attacks to some extent by increasing the co-occurring feature weights and providing features that are not captured by the P-Net. Furthermore, the dense connection, Ghost modules, and channel attention blocks (C-A blocks) are adopted to further strengthen the localization ability of PS-Net. Extensive experimental results illustrate that PS-Net can successfully distinguish forged regions in elaborate inpainted images, outperforming several state-of-the-art solutions. The proposed PS-Net is also robust against some postprocessing operations commonly used in Photoshop.