Existing anti-screen-shooting watermarking methods embed watermarks on either the server or client side. Server-side embedding incurs high computational and communication overhead under concurrent requests, while client-side methods risk watermark interception during transmission and require additional encrypted channels. To address these limitations, we propose an end-to-end separable watermarking framework (SepWater) that exploits latent space representations. By decoupling server-side watermark embedding from client-side image generation, SepWater enhances security by preventing leaks of both the original content and the watermark, while also reducing transmission costs. On the server side, a dedicated encoder processes the watermark information, while a frozen pre-trained encoder handles the original image. We then fuse their outputs into a compact latent vector for transmission to the client. On the client side, a frozen VQGAN decoder reconstructs the watermarked image directly. In addition, we propose a local residual attention loss, combined with other image quality constraints, to produce watermarked images with high capture resistance and visual fidelity. Furthermore, to improve the robustness of the SepWater, two noise modes are simulated that contain eye protection noise and lightweight edge grayscale deviation noise. Experiments show that SepWater outperforms state-of-the-art methods in withstanding screen-shooting distortions, optimizing communication efficiency, and scaling under high concurrency, making it suitable for practical deployment. The source code is released at https://github.com/CVhnu/SepWater.

Image hazing refers to adding haze to a clear image, which is important for improving the data amount and diversity of synthetic hazy images that are required to train deep image dehazing models. However, existing image hazing works generate hazy images from a given clear image with a single transmission map. This violates the fact that hazy images are diverse for a natural scene at different times. The domain shift issue between synthetic and real-world hazy images constrains the robustness of deep dehazing models when dealing with real-world hazy images. In this work, we propose an unsupervised haze generation work to synthesize multiple hazy images with diverse haze distributions from a clear image, which requires only an atmospheric scattering model without extra labeling information. Instead of estimating a transmission map from a clear image, we propose to customize the transmission maps by redefining the transmission function. In such a controllable way, hazy images with diverse haze distributions are generated, which avoids the labor-intensive collection of paired data and alleviates the common domain-shift issue of deep image dehazing. Incorporating the unsupervised hazy images generator, we also construct a generalizable self-supervised image dehazing (SSID) framework, where deep image dehazing models can be trained without any human annotations. Extensive experiments on real-world hazy images show that the proposed approach is superior to state-of-the-art unsupervised dehazing works, and achieves competitive performance with the supervised works. Moreover, the proposed SSID framework can be easily generalized to the existing deep dehazing models, greatly improving dehazing robustness on real-world hazy images.

Full-reference image dehazing quality assessment (FR-IDQA) evaluates the visual quality of a dehazed image by measuring its differences with a clear reference. The existing FR-IDQA methods are not convincing due to the lack of well-aligned datasets of hazy and clear image pairs and the limited hand-crafted features make it difficult to simulate the complicated perception by the human visual system (HVS). In this work, we build a real-world image dataset, namely RW-Haze, which comprises natural hazy images and their well-aligned clear references. Each clear image is paired with several hazy images with diverse haze levels from slight to heavy. Meanwhile, the existing FR-IDQA works evaluate the dehazed image quality in a global manner, without considering local haze distributions in the original hazy image. Actually, the perceived haze in a natural hazy image is not uniformly distributed, and the haze density varies with scene depth. Based on this priori observation, we design a haze density-aware convolutional neural network (CNN), namely DehIQA, for FR-IDQA. It adopts transfer learning to alleviate the issue of lacking sufficient labeled data. Specifically, we divide image dehazing assessment into two tasks. The source task is to classify unpaired clear and hazy images, which enforces the deep network to learn haze-related features. The target task is image quality assessment, which is achieved by transferring the trained model for the source task to the target task. Considering the fact that the perceived distortion in a dehazed image is also not uniform, we present a haze density-aware mechanism into DehIQA, which assigns different weights for different local regions in a dehazed image in terms of the dark channel of the original hazy image. Extensive experimental results show that DehIQA outperforms the state-of-the-art (SOTA) works on the benchmark dataset and achieves better consistency with human perceptions.

Image steganography is a technique used to conceal secret information within cover images without being detected. However, the advent of convolutional neural networks (CNNs) has threatened the security of image steganography. Due to the inherent properties of adversarial examples, adding perturbations to stego images can mislead the CNN-based image steganalysis, but it also easily leads to some errors when extracting secret information. Recently, some adversarial embedding methods have been proposed for improving image steganography security. In this work, we aim at furthering enhance the security of adversarial embedding-based image steganography by exploiting the strong correlation between adjacent pixels. Specifically, we divide the cover image into four non-overlapping parts for four-stage information embedding. During the adversarial embedding process, we cluster the modification directions of adjacent pixels and select only those with relatively larger amplitudes of gradients and smaller embedding costs to update their original embedding costs. Experimental results demonstrate that our proposed method can effectively fool targeted steganalyzers and outperform state-of-the-art techniques under different scenarios.

For Deepfake detection, residual-based features can preserve tampering traces and suppress irrelevant image content. However, inappropriate residual prediction brings side effects on detection accuracy. Meanwhile, residual-domain features are easily affected by some image operations such as lossy compression. Most existing works exploit either spatial-domain or residual-domain features, which are fed into the backbone network for feature learning. Actually, both types of features are mutually correlated. In this work, we propose an adaptive fusion based guided residuals network (AdapGRnet), which fuses spatial-domain and residual-domain features in a mutually reinforcing way, for Deepfake detection. Specifically, we present a fine-grained manipulation trace extractor (MTE), which is a key module of AdapGRnet. Compared with the prediction-based residuals, MTE can avoid the potential bias caused by inappropriate prediction. Moreover, an attention fusion mechanism (AFM) is designed to selectively emphasize feature channel maps and adaptively allocate the weights for two streams. Experimental results show that AdapGRnet achieves better detection accuracies than the state-of-the-art works on four public fake face datasets including HFF, FaceForensics++, DFDC and CelebDF. Especially, AdapGRnet achieves an accuracy up to 96.52% on the HFF-JP60 dataset, which improves about 5.50%. That is, AdapGRnet achieves better robustness than the existing works.

Most existing image dehazing approaches are able to achieve desirable results whose differences are too subtle for people to qualitatively judge. Therefore, it is important to adopt quantitative assessment on real-world hazy images. However, many dehazing works have not been quantitatively evaluated on real-world hazy images due to the lack of appropriate real-world datasets. In this work, we attempt to address the issue and present a well-aligned real-world benchmark dataset, namely RW-Haze, for image dehazing evaluation, which had been lacking for a long period of time. It contains 210 pairs of well-aligned haze-free images and hazy images with distinct haze densities, which were captured from six cities in China by fixed cameras. To the best of our knowledge, RW-Haze is the first real-world dataset that is made up of well-aligned image pairs of haze-free and hazy images with diverse haze levels. We select 13 state-of-the-art single image dehazing works for making comprehensive evaluations among them on RW-Haze dataset. Experimental results show that there still exist rich rooms for image dehazing research to improve its robustness on natural hazy images, especially dense haze scenes.

Deep learning-based Video Frame Interpolation (Deep VFI) diminishes the visual traces of the conventional one such that it is challenging for the current VFI detectors. Therefore, it is necessary to identify the presence of deep interpolated frames (DIF) in a video. This paper proposed a hybrid neural network to localize the DIF by learning spatio-temporal representations from the residual and motion vector information in the compression domain. Firstly, the residual and motion vector of motion regions are maintained by an intra-prediction constraints. Then, inherent tampering traces are further highlighted through subtracting the estimate of the residual or motion vector by virtue of residual modulation or MV refinement network. Finally, an attention-based dual-stream network is designed to jointly learn discriminative representations from the enhancement traces. Deep VFI video datasets created by the state-of-the-art deep VFI methods, have been evaluated, and extensive experimental results clearly demonstrate that our approach can achieve state-of-the-art performance compared with conventional methods.