Generative face video coding (GFVC) is vital for modern applications like video conferencing, yet existing methods primarily focus on video motion while neglecting the significant bitrate contribution of audio. Despite the well-established correlation between audio and lip movements, this cross-modal coherence has not been systematically exploited for compression. To address this, we propose an Audio-Visual Cross-Modal Compression (AVCC) framework that jointly compresses audio and video streams. Our framework extracts motion information from video and tokenizes audio features, then aligns them through a unified audio-video diffusion process. This allows synchronized reconstruction of both modalities from a shared representation. In extremely low-rate scenarios, AVCC can even reconstruct one modality from the other. Experiments show that AVCC significantly outperforms the Versatile Video Coding (VVC) standard and state-of-the-art GFVC schemes in rate-distortion performance, paving the way for more efficient multimodal communication systems.

With the rapid growth of generative AI and its widespread application in image editing, new risks have emerged regarding the authenticity and integrity of digital content. Existing versatile watermarking approaches suffer from tradeoffs between tamper localization precision and visual quality. Constrained by the limited flexibility of previous framework, their localized watermark must remain fixed across all images. Under AIGC-editing, their copyright extraction accuracy is also unsatisfactory. To address these challenges, we propose OmniGuard, a novel augmented versatile watermarking approach that integrates proactive embedding with passive, blind extraction for robust copyright protection and tamper localization. OmniGuard employs a hybrid forensic framework that enables flexible localization watermark selection and introduces a degradation-aware tamper extraction network for precise localization under challenging conditions. Additionally, a lightweight AIGC-editing simulation layer is designed to enhance robustness across global and local editing. Extensive experiments show that OmniGuard achieves superior fidelity, robustness, and flexibility. Compared to the recent state-of-the-art approach EditGuard, our method outperforms it by 4.25dB in PSNR of the container image, 20.7% in F1-Score under noisy conditions, and 14.8% in average bit accuracy.

This paper introduces Hierarchical Image Steganography (HIS), a novel method that uses diffusion models to enhance the security and capacity of embedding multiple images into a single container. HIS assigns varying levels of robustness to images based on their importance, ensuring enhanced protection against manipulation. It adeptly exploits the robustness of the Diffusion Model and the reversibility of the Flow Model. Integrating Embed-Flow and Enhance-Flow improves embedding efficiency and image recovery quality, respectively, setting HIS apart from conventional multi-image steganography techniques. This innovative structure can autonomously generate a container image, thereby securely and efficiently concealing multiple images and text. Rigorous subjective and objective evaluations underscore HIS’s advantage in analytical resistance, robustness, and capacity, illustrating its expansive applicability in content safeguarding and privacy fortification.

In the era of AI-generated content (AIGC), malicious tampering poses imminent threats to copyright integrity and information security. Current deep image watermarking, while widely accepted for safeguarding visual content, can only protect copyright and ensure traceability. They fall short in localizing increasingly realistic image tampering, potentially leading to trust crises, privacy violations, and legal disputes. To solve this challenge, we propose an innovative proactive forensics framework EditGuard, to unify copyright protection and tamper-agnostic localization, especially for AIGC-based editing methods. It can offer a meticulous embedding of imperceptible watermarks and precise decoding of tampered areas and copyright in-formation. Leveraging our observed fragility and locality of image-into-image steganography, the realization of Edit-Guard can be converted into a united image-bit steganography issue, thus completely decoupling the training process from the tampering types. Extensive experiments verify that our EditGuard balances the tamper localization accuracy, copyright recovery precision, and generalizability to various AIGC-based tampering methods, especially for image forgery that is difficult for the naked eye to detect.

AI-generated video has revolutionized short video production, filmmaking, and personalized media, making video local editing an essential tool. However, this progress also blurs the line between reality and fiction, posing challenges in multimedia forensics. To solve this urgent issue, V2A-Mark is proposed to address the limitations of current video tampering forensics, such as poor generalizability, singular function, and single modality focus. Combining the fragility of video-into-video steganography with deep robust watermarking, our method can embed invisible visual-audio localization watermarks and copyright watermarks into the original video frames and audio, enabling precise manipulation localization and copyright protection. We also design a temporal alignment and fusion module and degradation prompt learning to enhance the localization accuracy and decoding robustness. Meanwhile, we introduce a sample-level audio localization method and a cross-modal copyright extraction mechanism to couple the information of audio and video frames. The effectiveness of V2A-Mark has been verified on a visual-audio tampering dataset, emphasizing its superiority in localization precision and copyright accuracy, crucial for the sustainable development of video editing in the AIGC video era.

Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. Although several attempts have been made, most of them are limited to low-capacity and fixed steganography. To rectify these weaknesses, we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN) in this paper. For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN). Our method can hide/recover 7 secret videos in/from 1 cover video with promising performance. For flexibility, we propose a key-controllable scheme, enabling different receivers to recover particular secret videos from the same cover video through specific keys. Moreover, we further improve the flexibility by proposing a scalable strategy in multiple videos hiding, which can hide variable numbers of secret videos in a cover video with a single model and a single training session. Extensive experiments demonstrate that with the significant improvement of the video steganography performance, our proposed LF-VSN has high security, large hiding capacity, and flexibility. The source code is available at https://github.com/MC-E/LF-VSN.

Current image steganography techniques are mainly focused on cover-based methods, which commonly have the risk of leaking secret images and poor robustness against degraded container images. Inspired by recent developments in diffusion models, we discovered that two properties of diffusion models, the ability to achieve translation between two images without training, and robustness to noisy data, can be used to improve security and natural robustness in image steganography tasks. For the choice of diffusion model, we selected Stable Diffusion, a type of conditional diffusion model, and fully utilized the latest tools from open-source communities, such as LoRAs and ControlNets, to improve the controllability and diversity of container images. In summary, we propose a novel image steganography framework, named Controllable, Robust and Secure Image Steganography (CRoSS), which has significant advantages in controllability, robustness, and security compared to cover-based image steganography methods. These benefits are obtained without additional training. To our knowledge, this is the first work to introduce diffusion models to the field of image steganography. In the experimental section, we conducted detailed experiments to demonstrate the advantages of our proposed CRoSS framework in controllability, robustness, and security.

Image steganography aims to hide secret images into a container image, where the secret is hidden from human vision and can be restored when necessary. Previous image steganography methods are limited in hiding capacity and robustness, commonly vulnerable to distortion on container images such as Gaussian noise, Poisson noise, and lossy compression. This paper presents a novelflow-basedframe-work for robust invertible image steganography, dubbed as RIIS. A conditional normalizing flow is introduced to model the distribution of the redundant high-frequency component with the condition of the container image. Moreover, a well-designed container enhancement module (CEM) also contributes to the robust reconstruction. To regulate the net-work parameters for different distortion levels, a distortion-guided modulation (DGM) is implemented over flow-based blocks to make it a one-size-fits-all model. In terms of both clean and distorted image steganography, extensive experi-ments reveal that the proposed RIIS efficiently improves the robustness while maintaining imperceptibility and capacity. As far as we know, we are the first to propose a learning-based scheme to enhance the robustness of image steganog-raphy in the literature. The guarantee of steganography ro-bustness significantly broadens the application of steganog-raphy in real-world applications.

Providing high video quality under the lowest possible bitrate constraint is one of the critical challenges in video coding technology. Inspired by the continuous development of motion imitation [1], the model-based video coding method is derived from extracting a series of features or parameters representing the person's motion and reconstructing each frame by motion imitation model at the decoder. Thus, we propose a Semantic Neural Rendering-based Video Coding framework (SNRVC) to transmit video at ultra-low bitrate while maintaining high subjective quality. At the encoder, we first extract the motion parameters with specific semantic meanings from each frame and then compress the first frame and the parameters of the subsequent frames by truncating to different decimals and differential pulse code modulation coding. Finally, the decoded image and parameters are fed into the motion imitator [2] to obtain each reconstructed frame consistent with the movements of the original frame. Our SNRVC can achieve better visual quality than traditional and model-based methods [3] at the ultra-low bitrate below 0.01 bpp.

As an emerging compression scheme, conceptual coding usually encodes images into structural and textural representations and decodes them in a deep synthesis fashion. However, existing conceptual coding schemes ignore the structure of deep texture representation space, leading to a challenge of establishing efficient and faithful conceptual representations. In this paper, we firstly introduce contrastive learning into conceptual coding and propose Consistency-Contrast Learning (CCL) which optimizes the representation space by a consistency-contrast regularization. By modeling the original images and reconstructed images as "positive'' pairs and random images in a batch as "negative'' samples, CCL aims to align texture representation space with source images space relatively. Extensive experiments on diverse datasets demonstrate that: (1) the proposed CCL can achieve the best compression performance on the conceptual coding task; (2) CCL is superior to other popular regularization methods towards improving reconstruction quality; (3) CCL is general and can be applied to other tasks related to representation optimization and image reconstruction, such as GAN inversion.

Flow-based generative models are successfully applied in image generation tasks, where an invertible neural network (INN) is built up based on flow steps. Learning-based compression commonly transforms the input into a compact space and then implements a reconstruction network in the decoder accordingly. By utilizing low-resolution images, traditional or adaptive downsamplers with their corresponding traditional or learned upsamplers usually achieve better coding quality at low bit-rate. This paper proposes a novel image compression framework named Invertible Resampling-based Layered Image Compression (IRLIC). A rescaling network is built by splitting the input image into a downsampled image and a high-frequency part which is transformed into pre-defined distribution by INN, with symmetrical upsampling. Thus, reliable rescaling is applied in the total lossy compression framework, where only the downsampled image and the reconstruction residual are needed to recover a compressed image. Our IRLIC achieves superior performance to the current methods like BPG and other learning-based image compressions at the bit-rate below 1.8bpp.

This paper presents a novel encoding framework for the generative adversarial network (GAN), named compressive GAN inverter (Comp-GI), which can effectively compress, restore, and edit the attributes of real-world images. In particular, Comp-GI first transforms the input image into the latent variable space of GAN through an encoder network and then manipulates the latent code vector to enable various semantic image editing operations. StyleGAN [1] and its improved version StyleGANv2 [2] are adopted as the default GANs for image generation. Experiments on benchmark datasets FFHQ demonstrate that the proposed Comp-GI achieves better restoration performance with finer details and fewer artifacts than existing state-of-the-art GAN-inversion methods.