A verifiable privacy-preserving image retrieval scheme provides an effective approach for resource-constrained IoT devices to achieve secure image retrieval in untrusted cloud environments. However, existing schemes based on data structures still rely on verification structures provided by the cloud server during the verification process, introducing fundamental trust deficiencies. While blockchain-based schemes can ensure trustworthy results, they incur significant storage and computational overhead by undertaking the entire retrieval task. To overcome these limitations, a blockchain-assisted verifiable privacy-preserving image retrieval scheme in IoT environment is proposed in this paper. By adopting an innovative architecture where the retrieval task is retained by the cloud server and the verification task is delegated to the blockchain, a trusted verification mechanism independent of the cloud server is constructed. Specifically, an inverted indexing generation method based on piece-wise mean quantization is designed, enabling the blockchain to efficiently verify the completeness of the cloud server’s retrieval. A metadata generation method leveraging Pedersen commitments is developed to support batch integrity verification of retrieval results by users. Security analysis and experimental results demonstrate that our scheme satisfies security prerequisites and excels beyond existing solutions in terms of retrieval accuracy, search efficiency, verification efficiency, and storage overhead. On the real-world dataset, compared with existing scheme, our scheme demonstrates an 82% enhancement in search efficiency, a 36% improvement in verification efficiency, along with a 37% reduction in storage overhead.

Interleaved image-text generation has emerged as a vital multimodal task aimed at creating sequences of interleaved visual and textual content given a query. Despite notable advancements in recent multimodal large language models (MLLMs), generating integrated image-text sequences that exhibit narrative coherence and entity and style consistency remains challenging due to poor training data quality. To this end, we introduce CoMM, a high-quality Coherent interleaved image-text MultiModal dataset designed to enhance the coherence, consistency, and alignment of generated multimodal content. Initially, CoMM harnesses raw data from diverse sources, focusing on instructional content and visual storytelling, establishing a foundation for coherent and consistent content. To further refine the data quality, we devise a multi-perspective filter strategy that leverages advanced pre-trained models to ensure the development of sentences, consistency of inserted images, and semantic alignment between them. Various quality evaluation metrics are designed to prove the high quality of the filtered dataset. Meanwhile, extensive few-shot experiments on various downstream tasks demonstrate CoMM’s effectiveness in significantly enhancing the in-context learning capabilities of MLLMs. Moreover, we propose four new tasks to evaluate MLLMs’ interleaved generation abilities, supported by a comprehensive evaluation framework. We believe CoMM opens a new avenue for advanced MLLMs with superior multimodal in-context learning and understanding ability.

The boom of Generative AI brings opportunities entangled with risks and concerns. Existing literature emphasizes the generalization capability of deepfake detection on unseen generators, significantly promoting the detector’s ability to identify more universal artifacts. This work seeks a step toward a universal deepfake detection system with better generalization and robustness. We do so by first scaling up the existing detection task setup from the one-generator to multiple-generators in training, during which we disclose two challenges presented in prior methodological designs and demonstrate the divergence of detectors’ performance. Specifically, we reveal that the current methods tailored for training on one specific generator either struggle to learn comprehensive artifacts from multiple generators or sacrifice their fitting ability for seen generators (i.e., In-Domain (ID) performance) to exchange the generalization for unseen generators (i.e., Out-Of-Domain (OOD) performance). To tackle the above challenges, we propose our Discrepancy Deepfake Detector (D3) framework, whose core idea is to deconstruct the universal artifacts from multiple generators by introducing a parallel network branch that takes a distorted image feature as an extra discrepancy signal and supplement its original counterpart. Extensive scaled-up experiments demonstrate the effectiveness of D3, achieving 5.3% accuracy improvement in the OOD testing compared to the current SOTA methods while maintaining the ID performance. The source code will be updated in our GitHub repository: https://github.com/BigAandSmallq/D3.

BCube is a modular data center network.Compared with other topologies, BCube has natural advantages, such as lower deployment costs and stronger failure recovery capabilities.However, RDMA technology used in BCube still faces challenges, including high retransmission overhead, Head-of-Line Blocking (HoLB) and deadlock problems.Existing solutions for traditional data centers cannot simultaneously address these issues due to the unique topology and server transmission characteristics of BCube.In this paper, we propose a per-port flow control named PortFC for BCube.PortFC addresses the above problems through the designs of a Pause/Resume control signal, a per-port queue allocation method, an egress-detecting per-port flow control mechanism, and a serveraware queue scheduling method.Our evaluation shows that PortFC is free from retransmission, capable of eliminating HoLB and avoiding deadlocks.PortFC achieves 1.7-8.0times higher throughput and reduces latency by 11.7%-87.7%compared to the state-of-the-art

Originating from the diffusion phenomenon in physics that describes particle movement, the diffusion generative models inherit the characteristics of stochastic random walk in the data space along the denoising trajectory. However, the intrinsic mutual interference among image regions contradicts the need for practical downstream application scenarios where the preservation of low-level pixel information from given conditioning is desired (e.g., customization tasks like personalized generation and inpainting based on a user-provided single image). In this work, we investigate the diffusion (physics) in diffusion (machine learning) properties and propose our Cyclic One-Way Diffusion (COW) method to control the direction of diffusion phenomenon given a pre-trained frozen diffusion model for versatile customization application scenarios, where the low-level pixel information from the conditioning needs to be preserved. Notably, unlike most current methods that incorporate additional conditions by fine-tuning the base text-to-image diffusion model or learning auxiliary networks, our method provides a novel perspective to understand the task needs and is applicable to a wider range of customization scenarios in a learning-free manner. Extensive experiment results show that our proposed COW can achieve more flexible customization based on strict visual conditions in different application settings. Project page: https://wangruoyu02.github.io/cow.github.io/.