Abstract The rapid growth of multimodal content has introduced new challenges in cybersecurity, particularly in scenarios such as misinformation detection, multimedia forensics, and open-source intelligence. In these settings, verifying the consistency between textual descriptions and video content is critical, yet remains challenging due to noisy, incomplete, or even misleading multimodal signals. Text-to-video retrieval provides a important capability for cross-modal alignment. However, existing approaches often overlook a key issue: the usefulness of different modalities varies over time and depends on the query. In particular, audio signals can be informative in certain segments (e.g., speech) but misleading in others (e.g., background noise), making uniform fusion unreliable in security-critical scenarios. In this work, we propose Temporal Uncertainty-aware Retrieval (TUR), a unified framework that models text-video alignment from two complementary perspectives: temporal modality reliability and alignment uncertainty. TUR dynamically estimates the contribution of multimodal signals over time and adapts text representations according to cross-modal agreement, enabling more stable retrieval. Extensive experiments on MSR-VTT, DiDeMo, VATEX, and LSMDC demonstrate that TUR consistently outperforms prior methods. Further analysis shows that TUR achieves improved temporal grounding, more stable similarity estimation, and enhanced interpretability, which are desirable properties for security-sensitive applications.

Social network-based covert communication conceals the communication link between the sender and receiver, enabling one-to-many communication. Videos, due to their rich content and high embedding capacity, are ideal carriers for steganographic techniques. However, social networks typically apply lossy processing to uploaded videos, presenting significant challenges in constructing reliable covert communication channels. While prior research has proposed robust video steganographic methods, these approaches often rely on synchronization of robust regions to correctly extract hidden data. A major challenge arises when synchronization information is altered during lossy processing, complicating the accurate extraction of hidden data. To address this, a robust video steganographic framework is proposed. We then analyze the factors influencing the robustness of embedding units, including neighboring block differences, modulation types, and rate control modes. Based on this analysis, we introduce the Neighboring block Differences-based Cost Assignment (NDCA) method. Extensive experiments are conducted to demonstrate that the proposed framework and NDCA enhance robustness against lossy processing while maintaining high steganographic security. Furthermore, the robust video steganographic techniques based on the proposed framework and NDCA are broadly applicable to commonly used video encoders and rate control modes, enabling reliable covert communication on mainstream social networks.

Social networks provide an ideal channel for covert communication due to their one-to-many broadcasting nature and the concealment of communication links. Videos, with their rich content and high embedding capacity, serve as suitable carriers for steganography. However, video transcoding performed by social networks often invalidates traditional steganographic methods. To address this challenge, we propose a novel frame work based on optimized robust modulation paths. Specifically, we analyze the influence of modulation types on the robustness of embedding units, introduce a cost assignment method to quantify the embedding impact, and develop an optimization strategy to identify robust modulation paths. Experimental results demon strate that the proposed method achieves an average bit error rate below 0.5% across mainstream social networks, outperforming state-of-the-art methods in terms of robustness while maintaining sufficient steganographic security.

Abstract Most social media channels are lossy where videos are transcoded to reduce transmission bandwidth or storage space, such as social networking sites and video sharing platforms. Video transcoding makes most video steganographic schemes unusable for hidden communication based on social media. This paper proposes robust video steganography against video transcoding to construct reliable hidden communication on social media channels. A new strategy based on principal component analysis is provided to select robust embedding regions. Besides, side information is generated to label these selected regions. Side information compression is designed to reduce the transmission bandwidth cost. Then, one luminance component and one chrominance component are joined to embed secret messages and side information, notifying the receiver of correct extraction positions. Video preprocessing is conducted to improve the applicability of our proposed method to various video transcoding mechanisms. Experimental results have shown that our proposed method provides stronger robustness against video transcoding than other methods and achieves satisfactory security performance against steganalysis. Compared with some existing methods, our proposed method is more robust and reliable to realize hidden communication over social media channels, such as YouTube and Vimeo.

Due to a large number of user-uploaded images, social networks have become secure channels for covert communication. However, the JPEG recompression of social networks changes the DCT coefficients of stego images, resulting in the failure of adaptive steganography. To achieve steganography in lossy channels, robust steganography has been proposed. In this letter, the ability against JPEG recompression of robust steganography is further improved by introducing a robustness cost function. For calculating the robustness cost, a robustness model based on the spatial domain calculated from DCT coefficients is firstly proposed. Then the robustness cost is acquired by measuring the distance between the spatial pixels calculated from modified DCT coefficients and the robustness model adjusted spatial pixels. Combining the distortion function and the robustness cost function, the method proposed has considerable robustness performance while maintaining satisfying security performance. Experimental results show that with the maximum reduction of 4.04% on security performance, the algorithm proposed has a significant improvement on robustness performance compared with state-of-the-art robust steganography.

Sharing videos on social networks has become more and more popular, which provides a new scenario for covert communication. Video sharing-based hidden communication can conceal the contact relation between the sender and receiver and achieve the one-to-many delivery of secret messages. However, most video steganographic methods are unable to complete reliable hidden communication on social networks because of lossy video recompression. In this paper, we propose adaptive QIM (Quantization Index Modulation) with minimum embedding cost, which decreases the quantization distortion as much as possible to improve the security performance against steganalysis under the same level of robustness. Furthermore, based on the proposed quantization modulation scheme, we implement two robust steganographic methods in the DWT-SVD domain and the DTCWT-SVD domain. Experimental results show that the overall performance of our proposed modulation scheme outperforms QIM and adaptive QIM. Compared with existing robust video data hiding, two proposed steganographic methods demonstrate superior robustness and security on local lossy channels and social networks.

To cope with the abuse of steganography using H.264 videos, i.e., the dominant video format, as the carrier, this paper presents a steganalytic method which works well even in the scenario where both the training data and the prior knowledge of the test data are limited. As a key feature of H.264, intra prediction is incorporated to remove redundancies within one single frame by predicting the current block using previously coded blocks. Unlike in JPEG domain, the quantized discrete cosine transform (QDCT) coefficients in H.264 videos come from the prediction error (residual) blocks (PEBs) instead of the original pixel block, hence we suggest shifting the focal point from the spatial domain to the prediction error domain, i.e., the PEB domain. According to the traits of video coding, 3 types of subtractive PEB (SPEB) are defined to capture the inconsistency between correlated PEBs, and the differences between correlated SPEBs are modeled by first-order Markov chain. Then the so-called SUPERB (SUbtractive Prediction ERror Block) features are engineered by subsets of sample transition probability matrices for a steganalyzer. What's more, the features derived from IPM (Intra Prediction Mode) transition probabilities are also merged into SUPERB to improve detection ability. Extensive experiments are carried out from different aspects. Performance results demonstrate the effectiveness of SUPERB, particularly its essence of general applicability when the training and test data are of quite different attributes, which is more favorable for real-world applications.

Image steganalysis is a technique for detecting data hidden in images. Recent research has shown the powerful capabilities of using convolutional neural networks (CNN) for image steganalysis. However, due to the particularity of steganographic signals, there are still few reliable CNN-based methods for applying steganalysis to images of arbitrary size. In this paper, we address this issue by exploring the possibility of exploiting a network for steganalyzing images of varying sizes without retraining its parameters. On the assumption that natural image noise is similar between different image sub-regions, we propose an end-to-end, deep learning, novel solution for distinguishing steganography images from normal images that provides satisfying performance. The proposed network first takes the image as the input, then identifies the relationships between the noise of different image sub-regions, and, finally, outputs the resulting classification based upon them. Our algorithm adopts a Siamese, CNN-based architecture, which consists of two symmetrical subnets with shared parameters, and contains three phases: preprocessing, feature extraction, and fusion/classification. To validate the network, we generated datasets composed of steganography images with multiple sizes and their corresponding normal images sourced from BOSSbase 1.01 and ALASKA #2. Experimental results produced by the data generated by various methods show that our proposed network is well-generalized and robust.

The social networks transcode uploaded videos in a lossy way, which makes most video steganographic methods become unusable. In this paper, a robust video steganographic method is proposed to resist video transcoding on social networking sites. The luminance component of the raw video is selected as the cover and Quantization Index Modulation (QIM) algorithm based on block statistical features is applied to embed secret messages. To make a good tradeoff between the robustness and visual quality, an iteration in the local transcoder is designed to determine the minimum quantization step for each video. Then, a strategy of selecting robust video frames is proposed to further improve the robustness and security. To avoid sharing information beforehand between the sender and the receiver, a steganographic side channel is built for correct message extraction. Experimental results have shown that our proposed method can provide strong robustness against social networks transcoding, the average bit error rate is less than 1%. Meanwhile, our proposed method achieves a satisfactory level of security performance. It's a robust and secure method for covert communication on social networking sites such as YouTube and Vimeo.

In this paper, a novel deblocking filtering-based VP8 steganographic scheme is proposed. The unique aspect of this work and one that distinguishes it from the prior art is that we effectively exploit the characteristics of deblocking filtering. We propose to embed the secret messages by comparing the quantized discrete cosine transform coefficients before and after the in-loop filtering. In the process of encoding, given one frame, first, we encode it to obtain the quantized discrete cosine transform coefficients. Second, a new set of coefficients is obtained by re-encoding the filtered frame. Third, the distortion function is defined by comparing the difference between the two sets of coefficients. Finally, adaptive embedding is realized by using the syndrome-trellis codes. Experimental results show that satisfactory levels of visual quality and steganographic security could be achieved with adequate payloads.

Moving steganography and steganalysis from the laboratory into the real world, the robustness of steganography needs to be further considered. In this paper, we propose a robust steganographic algorithm to resist the JPEG compression of transport channel based on transport channel matching. Transport channel matching can adjust images to meet the requirements of transport channel so that the impact of JPEG compression from the channel can be reduced. To improve the robustness of steganography, the embedded message bits will be encoded by the error correction code. Then, the adaptive steganographic algorithms will be used to embed messages. To enhance the coding rate, the error correction capability t of the error correction code is dynamically adjusted according to the images. Experimental results on the local simulation of JPEG compression and social network site demonstrate that the proposed steganographic algorithm has a good performance with respect to both robustness and security.

This paper presents a steganalytic approach against motion vector-based video steganography that does not depend on the detailed knowledge of embedding algorithms. In most state-of-the-art video coding standards, the motion vector is the result of block-based motion estimation using rate-distortion optimization. That is to say, each motion vector is locally optimal in a rate-distortion sense, and any modification will inevitably shift the motion vector from locally optimal to non-optimal. As a consequence, it is a very strong evidence of steganography if some motion vectors are found to be locally non-optimal. Based on this fact, the core of our method is an estimator to check the local optimality of motion vectors in a rate-distortion sense. We try to recover the necessary information used for motion vector decision that is lost during lossy compression, based on which a 36-D feature set is formed for training and classification. To demonstrate the effectiveness of the proposed approach, experiments are carried out in different settings. The corresponding results show that our approach has a wide applicability even at low embedding strengths. Particularly, the problem of cover source mismatch is largely alleviated, which indicates that the proposed approach is suitable to be used in situations where a very limited priori knowledge is available.

This paper presents an effective motion vector (MV)-based steganography to cope with different steganalytic models. The main principle is to define a distortion scale expressing the multi-level embedding impact of MV modification. Three factors including motion characteristic of video content, MV's local optimality and statistical distribution are considered in distortion definition. For every embedding location, the contributions of three factors are dynamically adjusted according to MV's property. Based on the defined distortion function, two layered syndrome-trellis codes (STCs) are utilized to minimize the overall embedding impact in practical embedding implementation. Experimental results demonstrate that the proposed method achieves higher level of security compared with other existing MV-based approaches, especially for high quality videos.

In this paper, a novel motion vector-based video steganographic scheme is proposed, which is capable of withstanding the current best statistical detection method. With this scheme, secret message bits are embedded into motion vector (MV) values by slightly perturbing their motion estimation (ME) processes. In general, two measures are taken for steganographic security (statistical undetectability) enhancement. First, the ME perturbations are optimized ensuring the modified MVs are still local optimal, which essentially makes targeted detectors ineffective. Secondly, to minimize the overall embedding impact under a given relative payload, a double-layered coding structure is used to control the ME perturbations. Experimental results demonstrate that the proposed scheme achieves a much higher level of security compared with other existing MV-based approaches. Meanwhile, the reconstructed visual quality and the coding efficiency are slightly affected as well.

In this paper, with a novel data representation named macroblock partition mode, an effective steganography integrated with H.264/AVC compression is proposed. The main principle is to improve the steganographic security in two directions. First, to embed messages, an internal process of H.264 compression, i.e., the macroblock partition, is slightly perturbed, hence the compression compliance is ensured. Second, to minimize the embedding impact, a high efficient double-layered structure is deliberately designed. In the first layer, the syndrome-trellis codes (STCs) is utilized to perform adaptive embedding, and the costs in visual quality and compression efficiency are both considered to construct the distortion model. In the second layer, facilitated by the wet paper codes (WPCs), an expected 3-bit per change gain in embedding efficiency is obtained.