ABSTRACT Multimodal fake news detection (MFND) has attracted growing attention as misinformation increasingly appears in heterogeneous forms that combine text, images, audio, video, and social context, while recent generative models further increase the realism and scalability of deceptive content. Meanwhile, the rise of large language models (LLMs) and multimodal large language models (MLLMs) has introduced new opportunities as well as new evaluation challenges for MFND. In this survey, we present a structured review of the field through a taxonomy that groups existing methods into two broad paradigms: small‐model‐based methods and LLM‐involved methods. For each paradigm, we analyse representative approaches from the perspectives of modelling strategy, factual grounding, robustness, and deployment feasibility. We also review widely used datasets and evaluation protocols, and discuss their limitations with respect to modality composition, task heterogeneity, and result comparability. Finally, we outline important open problems and future directions, including the detection of LLM‐generated misinformation, cross‐lingual generalisation, interpretable and evidence‐grounded reasoning, and trustworthy evaluation in realistic deployment settings.

With the emergence of adversarial steganography, existing specialized steganalysis models suffer a significant performance decline in detecting non-homologous adversarial steganographic methods (i.e., trained on traditional-based method and tested on adversarial-based method), resulting in insufficient robustness in complex network environments. To address this issue, we propose a two-stage robust steganalysis framework with multi-feature enhancement against adversarial steganography. The framework integrates edge-aware attention with multi-dimensional statistical features to enhance robustness against adversarial steganography. In the first stage, we design a covariance pooling based convolutional neural network and integrate an edge-aware attention mechanism to improve the feature representation of subtle steganographic traces, enabling fast detection for most samples. In the second stage, samples with uncertain confidence scores from the first stage are further analyzed by extracting block-wise entropy features, global entropy features, and SRM co-occurrence features, followed by dimensionality reduction via principal component analysis (PCA) and classification using a random forest. The final decision is made through the collaborative fusion of the two stages. Experimental results demonstrate that the proposed method achieves excellent detection performance (2.57% average improvement over the existing best method) with strong robustness for adversarial steganography, and its generalization capability is further validated in cross-dataset scenarios. Furthermore, comprehensive ablation studies validate the efficacy of the network architecture.

In the context of Internet of Things edge devices, pretrained models are often sourced directly from cloud computing platforms due to the unavailability of training data. This lack of access during the training phase makes these models susceptible to backdoor attacks. To address this challenge, we introduce a novel data-free backdoor removal method that operates effectively even when only the poisoned model is accessible. Our innovative approach employs two end-to-end generators with identical architectures to create both clean and poisoned samples. These samples are crucial for transferring knowledge from the teacher model—the fixed poisoned model—to the student model, which is initialized with the poisoned model. Our method utilizes a channel shuffling technique during the distillation process to disrupt and eliminate the backdoor knowledge embedded in the teacher model. This process involves iterative updates of the generators and meticulous distillation of the student model, leading to efficient backdoor removal. We conducted extensive experiments on five sophisticated backdoor attacks across two benchmark datasets. The results demonstrate that our method not only significantly bolsters the model’s resistance to backdoor attacks but also maintains high recognition accuracy for clean samples, thereby outperforming existing methods. Additionally, the code for our method is available athttps://github.com/gaoyafeiyoo/ADBR.

With the widespread adoption of encrypted communication and anonymous networks, traditional passive traffic analysis methods face considerable limitations in tracking malicious activities. Existing active network flow watermarking schemes, exhibit insufficient robustness against packet dropping and splitting attacks. In response, this paper introducesAverage Interval-based Watermarking (AIBW), a novel technique designed to enhance watermark resilience by partitioning network flows into discrete time windows and dynamically adjusting inter-packet intervals. Specifically, AIBW categorizes packets into three segments—start,information, andend—and embeds watermark bits through maximum/minimum delay modulation within the information segment. Experimental evaluations demonstrate that AIBW outperforms state-of-the-art watermarking methods, yielding average accuracy improvements.

Since the generative adversarial network (GAN) was proposed by Ian Goodfellow et al. in 2014, it has been widely used in various fields. However, there are only a few works related to image steganography so far. Existing GAN-based steganographic methods mainly focus on the design of generator, and just assign a relatively poorer steganalyzer in discriminator, which inevitably limits the performances of their models. In this paper, we propose a novel Steganographic method based on Generative Multi-Adversarial Network (Steg-GMAN) to enhance steganography security. Specifically, we first employ multiple steganalyzers rather than a single steganalyzer like existing methods to enhance the performance of discriminator. Furthermore, in order to balance the capabilities of the generator and the discriminator during training stage, we propose an adaptive way to update the parameters of the proposed GAN model according to the discriminant ability of different steganalyzers. In each iteration, we just update the poorest one among all steganalyzers in discriminator, while update the generator with the gradients derived from the strongest one. In this way, the performance of generator and discriminator can be gradually improved, so as to avoid training failure caused by gradient vanishing. Extensive comparative results show that the proposed method can achieve state-of-the-art results compared with the traditional steganography and the modern GAN-based steganographic methods. In addition, a large number of ablation experiments verify the rationality of the proposed model.

The recent literature has shown that adversarial embedding has promise for enhancing the security of steganography. However, existing methods achieve the final stego mainly based on a pre-trained Convolutional Neural Network (CNN)-based steganalyzer without considering any other steganalytic features. When the steganalyzer is re-trained, its performance usually drops significantly. We propose a novel adversarial embedding method via stego generation and selection. To improve the diversity of the stego images, this method first randomly generates many candidate stegos according to the amplitudes of the gradients and embedding costs of a given cover. Since the image residuals are the commonly used low-level features in many steganalyzers, the proposed method carefully designs different adaptive high-pass filters to calculate the image residuals, and then selects a final stego from among those candidate stegos which can successfully fool the pre-trained steganalyzer, according to the residual distance between stego and the cover. Extensive experimental evaluations on re-trained CNN-based and traditional steganalyzers demonstrate that the proposed method can significantly enhance the security of the modern steganographic methods in both spatial and JPEG domains, and achieve much better performance than related adversarial embedding methods.

Unlike most existing steganography methods which are mainly focused on designing embedding cost, in this paper, we propose a new method to enhance existing steganographic methods via stego generation and selection. The proposed method firstly trains a steganalytic network according to the steganography to be enhanced, and then tries to adjust a tiny part of original embedding costs based on the magnitudes of it and the corresponding gradients obtained from the pre-trained network, and generates many candidate stegos in a random manner. Finally, the method selects a stego according to its image residual distance to cover. Extensive experimental results have shown that the proposed method can siginficantly enhance the security performance of current steganography in spatial domain against four steganalytic classifiers. In addition, comparative analysis between original stegos and the resulting ones with the proposed method are given.

Image steganography aims to embed secret messages into cover images in an imperceptible manner. While steganalysis tries to identify stegos from covers, which is a special binary classification problem. Recently, some literatures show that the adversarial embedding can mislead the advanced steganalyzers based on convolutional neural network (CNN), and thus enhance the steganography security. Since adding perturbations to stegos may lead to messages extraction failure due to properties of syndrome-trellis codes (STC), the existing adversarial examples are derived from covers or their enhanced versions, while those stegos are not fully utilized. In this paper, we propose a new adversarial embedding scheme for image steganography. Unlike those related works, we first combine multiple gradients of cover and generated stegos to determine the directions of cost modifications. Next, instead of adjusting all or a random part of embedding costs in existing works, we carefully select the candidate costs according to the amplitudes of cover gradients and their costs. Extensive experimental results demonstrate that by adjusting a tiny part of embedding costs (less than 5% in most cases), the proposed method can significantly improve the security of five modern steganographic methods evaluated on both re-trained CNN-based and traditional steganalyzers, and achieve much better security performances compared with related methods. In addition, the security performances evaluated on different image database show that the generalization of the proposed method is good.