Advances in image generation enable hyper-realistic synthetic faces but also pose risks, thus making synthetic face detection crucial. Previous research focuses on the general differences between generated images and real images, often overlooking the discrepancies among various generative techniques. In this paper, we explore the intrinsic relationship between synthetic images and their corresponding generation technologies. We find that specific images exhibit significant reconstruction discrepancies across different generative methods and that matching generation techniques provide more accurate reconstructions. Based on this insight, we propose a Multi-Reconstruction-based detector. By reversing and reconstructing images using multiple generative models, we analyze the reconstruction differences among real, GAN-generated, and DM-generated images to facilitate effective differentiation. Additionally, we introduce the Asian Synthetic Face Dataset (ASFD), containing synthetic Asian faces generated with various GANs and DMs. This dataset complements existing synthetic face datasets. Experimental results demonstrate that our detector achieves exceptional performance, with strong generalization and robustness.

Graphics Interchange Format (GIF) encoding is the art of reproducing an image with limited colors. Existing GIF encoding schemes often introduce unpleasant visual artifacts such as banding artifact, dotted-pattern noise and color shift, especially when the palette size is small. To address the issues above, we propose VivID, a Visually Improved GIF Encoding Network Design, which is compatible with exiting GIF decoders. VivID consists of three modules and two of them provide the functionality within the GIF encoding pipeline. Firstly, in order to reduce the color shift introduced by color quantization, we design the multi-palette extractor to create a GIF image with minimal distortion by extracting a near-optimal palette. This module can significantly improve the image fidelity and gains adaptability to multiple palette sizes after only one-time training. Furthermore, to reduce banding artifact and the dotted-pattern noise caused by dithering process, we propose banding remover which can randomize quantization error to neighbourhood by utilizing a learnable dithering pattern. Moreover, to further eliminate the banding artifacts, we design the banding scorer module, which is a novel metric for evaluating banding artifact and it correlates well with subjective perception. We adopt it as a customized loss for training dithering module. Extensive experiments across various aspects demonstrate that VivID produces visually pleasing results even when the palette size is extremely small, outperforming both traditional and existing learning based GIF encoding methods.

Abstract Deep neural network model extraction attack is the process of retraining a surrogate model based on the outputs of a target model with a given set of inputs. Such attacks are hard to defend for the sake of model owners’ interest. Recently, some work propose model watermarking scheme for image processing networks, which is able to prove the intellectual property of deep models even after the model extraction attack. This scheme makes sure that, once the target model (an image processing network) is watermarked, we can extract the watermark from the output of the surrogate model. In this paper, we propose a new model extraction attack scheme to fight against the latest method. Instead of directly using the output images of a target model, we propose to use their reconstructed versions for model retraining, where an asymmetrical UNet is proposed for image reconstruction. To thoroughly remove the watermarking traces, we propose and incorporate a referenced subspace attention module in the asymmetrical UNet, which removes the watermark by projecting the outputs of the target model into the subspaces of the reference image. Various experiments demonstrate the effectiveness of our attack.

With the development of robust information hiding (RIH) approaches, secret messages can be extracted successfully from stego-data after transmission through lossy channels of online social networks (OSNs). To interrupt illegal covert communications in OSNs, some methods sanitize the uploaded images by image processing operations to destroy the hidden data that may exist. However, none of the existing methods takes the RIH methods that can resist scaling into consideration, while scaling is a common operation in OSNs. In this paper, we first propose a general framework for image sanitization in OSN platforms, which serves as a countermeasure against the RIH. By using such a framework, the secret messages embedded in the upload images can be removed and the quality of the sanitized image can be well maintained. Our framework contains two deep neural networks: Scaling-Net and SC-Net. The Scaling-Net is dedicated to the sanitization of oversized images while the SC-Net is designed for other images. To achieve a good image quality, we also propose a discriminator for adversarial training of the Scaling-Net and SC-Net. Experimental results on different datasets demonstrate that our proposed method outperforms the state-of-the-art methods. The source code and pretrained models are available at our code repository (https://github.com/zyzhu19/Image_Sanitization).

Some robust data hiding approaches have been proposed to transmit secret data through online social networks (OSNs). Traditional steganalysis tools are inefficient in detecting these tailored steganographic methods. Although some algorithms have been developed to remove the hidden data of images, they are criticized for their low secret removal rate and poor image quality. Moreover, most of them are nongeneric methods, and multiple models must be trained to fit different algorithms. In this letter, we propose a general end-to-end data hiding break approach for OSNs, called the secret data remover (SDR). It is universal for algorithms of both robust steganography and robust watermarking, with which stego images are directly input and clean ones with the same appearances will then be generated. Moreover, we develop two novel techniques, namely, image fusion and latent renewal, to enhance the image quality and improve the overall performance. Experiments show that our proposed method achieves superior performance compared to state-of-the-art works. Hidden secret data are cleared while image quality is maintained or even slightly improved. At the same time, our work can be easily deployed in OSNs.

Abstract Animated emoji is a kind of GIF image, which is widely used in online social networks (OSN) for its efficiency in transmitting vivid and personalized information. Aiming at realizing covert communication in animated emoji, this paper proposes an improved steganography framework in animated emoji. We propose a self-reference algorithm to improve the steganography security. Meanwhile, the relations between adjacent frames of the cover GIF image are considered to further improve the distortion function. After that we embed the secret message into the GIF image using the popular framework of Syndrome Trellis Coding (STC). Experimental results show that the proposed method can provide better security performances than state-of-the-art works.