Invisible image watermarking plays a critical role in safeguarding AI-generated images, yet current removal methods face practical limitations in real-world settings. They compromise image quality, are tailored to specific watermarking schemes, or depend on original-watermarked image pairs. These limitations hinder reliable evaluations of watermark robustness. In this work, we propose a universal and quality-preserving watermark removal method based on unpaired learning. Specifically, we implement a three-stage training framework in which we first pre-train the remover to denoise corrupted images. Then the discriminator is trained to distinguish watermarked images from original ones. Finally, the remover and the discriminator are jointly trained in an adversarial manner to further strengthen the watermark elimination capability of the remover while enhancing image quality. Evaluated across various watermarking schemes, our method achieves watermark extraction error rates close to random guessing while maintaining high visual quality. This work reveals that most existing watermarking methods lack sufficient robustness.

Combating counterfeit products is crucial for maintaining a healthy market. Recently, Copy Sensitive Graphical Codes (CSGC) have garnered significant attention due to their high sensitivity to illegal physical copying. Copy Detection Patterns (CDP) and Two-Level QR Codes (2LQR code) are two representative methods. CDP offers high efficiency and low cost, enabling use in document authentication and product anti-counterfeiting, and has achieved broad commercial adoption. In contrast, 2LQR code, as a consumer-grade document authentication solution, provides additional private message sharing functionalities. We observe that both the CDP and 2LQR code can be synthesized using textured patterns. To this end, we propose a flexible framework that integrates the stochastic anti-counterfeiting properties of CDP with the private message sharing of 2LQR code. Specifically, we model CDP as a random noise image composed of multiple textured patterns similar to those in 2LQR code, where each pattern represents an informative digit. Thus, both codes can be generated through textured pattern design. We formulate this as a constrained optimization framework called Mixed-Bit Sampling Marking (MSM). The objective incorporates white pixel ratio and spatial randomness, with constraints defined by a flexible modulation function (e.g., DCT or Pearson similarity), customizable to user needs. A two-step sampling algorithm solves the optimization. We demonstrate CDP and 2LQR codes generated via MSM and validate their ability to inherit advantages from both approaches. Experiments show that MSM-generated texture patterns effectively synthesize both CDPs and 2LQR codes, preserving their advantages while offering a novel, flexible solution for document authentication.

Camera model identification (CMI) can be widely used in image forensics such as authenticity determination, copyright protection, forgery detection, etc. Meanwhile, with the vigorous development of the Internet, online social networks (OSNs) have become the dominant channels for image sharing and transmission. However, the inevitable lossy operations on OSNs, such as compression and post-processing, impose great challenges to the existing CMI schemes, as they severely destroy the camera traces left in the images under investigation. In this work, we propose a novel CMI method that is robust against the lossy operations of various OSN platforms. Specifically, it is observed that a camera trace extractor can be easily trained on a single degradation scenario (e.g., one specific OSN platform); while much more difficult on mixed degradation scenarios (e.g., multiple OSN platforms). Inspired by this observation, we design a new multi-scenario learning (MSL) strategy, enabling us to extract robust camera traces across different OSNs. Furthermore, noticing that image smooth regions incur less distortions by OSN and less interference by image signal itself, we suggest a SmooThness-Aware Trace Extractor (STATE) that can adaptively extract camera traces according to the smoothness of the input image. The superiority of our method is verified by comparative experiments with four state-of-the-art methods, especially under various OSN transmission scenarios. Particularly, for the open-set camera model verification task, we greatly surpass the second-place by 15.30% in AUC on theFODBdataset; while for the close-set camera model classification task, we are significantly ahead of the second-place by 34.51% in F1 on theSIHDRdataset. The code of our proposed method is available athttps://github.com/HighwayWu/CameraTraceOSN.

With the increasing prevalence of cloud computing platforms, ensuring data privacy during the cloud-based image-related services such as classification has become crucial. In this study, we propose a novel privacy-preserving image classification scheme that enables the direct application of classifiers trained in the plaintext domain to classify encrypted images without the need of retraining a dedicated classifier. Moreover, encrypted images can be decrypted back into their original form with high fidelity (recoverable) using a secret key. Specifically, our proposed scheme involves utilizing a feature extractor and an encoder to mask the plaintext image through a newly designed Noise-like Adversarial Example (NAE). Such an NAE not only introduces a noise-like visual appearance to the encrypted image but also compels the target classifier to predict the ciphertext as the same label as the original plaintext image. At the decoding phase, we adopt a Symmetric Residual Learning (SRL) framework for restoring the plaintext image with minimal degradation. Extensive experiments demonstrate that (1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; (2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; (3) our system exhibits satisfactory generalization capability on the encryption, decryption, and classification tasks across datasets that are different from the training one; and (4) a high-level of security is achieved against three potential threat models. The code is available at https://github.com/csjunjun/RIC.git .

Online Social Networks (OSNs) have blossomed into prevailing transmission channels for images in the modern era. Adversarial examples (AEs) deliberately designed to mislead deep neural networks (DNNs) are found to be fragile against the inevitable lossy operations conducted by OSNs. As a result, the AEs would lose their attack capabilities after being transmitted over OSNs. In this work, we aim to design a new framework for generating robust AEs that can survive the OSN transmission; namely, the AEs before and after the OSN transmission both possess strong attack capabilities. To this end, we first propose a differentiable network termed SImulated OSN (SIO) to simulate the various operations conducted by an OSN. Specifically, the SIO network consists of two modules: (1) a differentiable JPEG layer for approximating the ubiquitous JPEG compression and (2) an encoder-decoder subnetwork for mimicking the remaining operations. Based upon the SIO network, we then formulate an optimization framework to generate robust AEs by enforcing model outputs with and without passing through the SIO to be both misled. Extensive experiments conducted over Facebook, WeChat and QQ demonstrate that our attack methods produce more robust AEs than existing approaches, especially under small distortion constraints; the performance gain in terms of Attack Success Rate (ASR) could be more than 60%. Furthermore, we build a public dataset containing more than 10,000 pairs of AEs processed by Facebook, WeChat or QQ, facilitating future research in the robust AEs generation. The dataset and code are available at https://github.com/csjunjun/RobustOSNAttack.git .

Selling counterfeit online has become a serious problem, especially with the advancement of social media and mobile technology. Instead of investigating the products directly, one can only check the images, tags annotated by the sellers on the images, or the price to decide if a seller sells counterfeits. One of the ways to detect counterfeit sellers is to investigate their social graphs, in which counterfeit sellers show different behaviour in network measurements, such as those in centrality and EgoNet. However, social graphs are not easily accessible. They may be kept private by the operators, or there are no connections at all. This article proposes a framework to detect counterfeit sellers using their connection graphs discovered from their shared images. Based on 153 K shared images from Taobao, it is proven that counterfeit sellers have different network behaviours. It is observed that the network measurements follow Beta function well. Those distributions are formulated to detect counterfeit sellers by the proposed framework, which is 60% better than approaches using classification.

Screen-shooting resilient (SSR) watermark is a special kind of robust watermarking. One can extract the watermark message even the embedded image communicates via a physical screen to the camera channel. The keypoint-based SSR watermarking is one promising solution to realize such screen-to-camera communication. The enhanced keypoints were used to locate the embedding region and then perform watermark embedding. However, the keypoint-based SSR watermarking treats the critical two steps, keypoint enhancement and watermark embedding, independently, neglecting their inter-play. This work proposes a watermark-preserving keypoint enhancement algorithm for SSR watermarking. Specifically, we resort to a convex constrained optimization framework to unify keypoint enhancement and watermark embedding. Multiple constraints are imposed to simultaneously ensure the watermark validity and blind synchronization of embedding regions. Our method enables jointly optimizing the watermarking distortion and keypoint enhancement. The proposed method achieves superior watermark extraction accuracy while retaining better watermarked image quality when compared with previous works.

Image manipulation chain detection aims to identify the existence of involved operations and also their orders, playing an important role in multimedia forensics and image analysis. However,all the existing algorithms model the manipulation chain detection as a classification problem, and can only detect chains containing up to two operations. Due to the exponentially increased solution space and the complex interactions among operations, how to reveal a long chain from a processed image remains a long-standing problem in the multimedia forensic community. To address this challenge, in this paper, we propose a new direction for manipulation chain detection. Different from previous works, we treat the manipulation chain detection as a machine translation problem rather than a classification one, where we model the chains as the sentences of a target language, and each word serves as one possible image operation. Specifically, we first transform the manipulated image into a deep feature space, and further model the traces left by the manipulation chain as a sentence of a latent source language. Then, we propose to detect the manipulation chain through learning the mapping from the source language to the target one under a machine translation framework. Our method can detect manipulation chains consisting of up to five operations, and we obtain promising results on both the short-chain detection and the long-chain detection.

Social events are very common activities, where people can interact with each other. During an event, the organizer often hires photographers to take images, which provide rich information about the participants’ behaviour. In this work, we propose a method to discover the social graphs among event participants from the event images for social network analytics. By studying over 94 events with 32,330 event images, it is proven that the social graphs can be effectively extracted solely from event images. It is found that the discovered social graphs follow similar properties of online social graphs; for instance, the degree distribution obeys power law distribution. The usefulness of the proposed method for social graph discovery from event images is demonstrated through two applications: important participants detection and community detection. To the best of our knowledge, it is the first work to show the feasibility of discovering social graphs by utilizing event images only. As a result, social network analytics such as recommendations become possible, even without access to the online social graph.

In recent years, online social networks (OSNs) have become extremely popular and been one of the most common ways for storing and distributing images. Naturally, such widespread availability of OSN makes it a viable channel for transmitting additional data along with the image sharing. However, various lossy operations, e.g., resizing and compression, conducted by OSN platforms impose great challenges for designing a robust watermarking scheme over OSN shared images. In this paper, we tackle this challenge and propose a robust high-capacity watermarking technique, by using Facebook as a representative OSN. To achieve the satisfactory robustness, we first probe into Facebook and recover the image manipulation mechanism via a deep convolutional neural network (DCNN) approach. Assisted with the precise knowledge on the lossy channel offered by Facebook, we then suggest a DCT-domain image watermarking method that is highly robust against the lossy operations on Facebook, even without any error correcting codes (ECC). The proposed technique is also extended to other popular OSNs, e.g., Wechat and Twitter. Extensive experimental results are provided to show the superior performance of our method in terms of the embedding capacity, data extraction accuracy, and quality of the reconstructed images.

Online Social Networks (OSNs) have attracted a huge number of users, who store and share various images on a daily basis. As a well-known fact, most OSN platforms apply a series of lossy operations on the uploaded images, which could severely degrade the quality of the shared images, negatively affecting the user experiences. In this work, we consider the problem of significantly improving OSN-shared images through applying an optimal pre-filtering prior to image sharing, without any cooperation from the OSN platform itself. Facebook, as one of the most popular and representative OSNs, is chosen as the platform to present our designed pre-filtering strategy. We first treat Facebook as a black box, and thoroughly recover its mechanism of processing color images. Based on the precise knowledge on the image processing pipeline on Facebook, we design the pre-filter under an optimization framework, minimizing the end-to-end distortion between the shared image and the original one. Compared with the directly shared images, our proposed pre-filtering-then-sharing strategy brings significant improvements in terms of both quantitative and qualitative metrics. Extensive experimental results are provided to show the superiority of our proposed method. Finally, we discuss the strategy on how to extend our proposed technique to other OSN platforms.

With the advancement of social media and mobile technology, any smartphone user can easily become a seller on social media and e-commerce platforms, such as Instagram and Carousell in Hong Kong or Taobao in China. A seller shows images of their products and annotates their images with suitable tags that can be searched easily by others. Those images could be taken by the seller, or the seller could use images shared by other sellers. Among sellers, some sell counterfeit goods, and these sellers may use disguising tags and language, which make detecting them a difficult task. This article proposes a framework to detect counterfeit sellers by using deep learning to discover connections among sellers from their shared images. Based on 473K shared images from Taobao, Instagram, and Carousell, it is proven that the proposed framework can detect counterfeit sellers. The framework is 30% better than approaches using object recognition in detecting counterfeit sellers. To the best of our knowledge, this is the first work to detect online counterfeit sellers from their shared images.

Sharing images online has become extremely easy and popular due to the ever-increasing adoption of mobile devices and online social networks (OSNs). The privacy issues arising from image sharing over OSNs have received significant attention in recent years. In this article, we consider the problem of designing a secure, robust, high-fidelity, storage-efficient image-sharing scheme over Facebook, a representative OSN that is widely accessed. To accomplish this goal, we first conduct an in-depth investigation on the manipulations that Facebook performs to the uploaded images. Assisted by such knowledge, we propose a DCT-domain image encryption/decryption framework that is robust against these lossy operations. As verified theoretically and experimentally, superior performance in terms of data privacy, quality of the reconstructed images, and storage cost can be achieved.

With the ever-increasing popularity of mobile devices and online social networks (OSNs), sharing photos online has become extremely easy and popular. The privacy issues of shared photos and the associated protection schemes have received significant attention in recent years. In this work, we address the problem of designing privacy-preserving, high-fidelity, storage-efficient photo sharing solution over Facebook. We first conduct an in-depth study on the manipulations that Facebook performs to the uploaded images. With the awareness of such information, we suggest a DCT-domain image encryption scheme that is robust against these lossy operations. As validated by our experimental results, superior performance in terms of security, quality of the reconstructed images, and storage cost can be achieved.

This paper proposes a novel reversible image data hiding scheme over encrypted domain. Data embedding is achieved through a public key modulation mechanism, in which access to the secret encryption key is not needed. At the decoder side, a powerful two-class SVM classifier is designed to distinguish encrypted and nonencrypted image patches, allowing us to jointly decode the embedded message and the original image signal. Compared with the state-of-the-art methods, the proposed approach provides higher embedding capacity and is able to perfectly reconstruct the original image as well as the embedded message. Extensive experimental results are provided to validate the superior performance of our scheme.