Light Field Depth Estimation Based on Stitched-EPI

Depth estimation is one of the most essential problems for light field applications. In EPI-based methods, the slope computation usually suffers low accuracy due to the discretization error and low angular resolution. In addition, recent methods work well in most regions but often struggle with blurry edges over occluded regions and ambiguity over texture-less regions. To address these challenging issues, we first propose the stitched-EPI and half-stitched-EPI algorithms for non-occluded and occluded regions, respectively. The algorithms improve slope computation by shifting and concatenating lines in different EPIs but related to the same point in 3D scene, while the half-stitched-EPI only uses non-occluded part of lines. Combined with the joint photo-consistency cost proposed by us, the more accurate and robust depth map can be obtained in both occluded and non-occluded regions. Furthermore, to improve the depth estimation in texture-less regions, we propose a depth propagation strategy that determines their depth from the edge to interior, from accurate regions to coarse regions. Experimental and ablation results demonstrate that the proposed method achieves accurate and robust depth maps in all regions effectively.Comment: 15 page

FederBoost: Private Federated Learning for GBDT

An emerging trend in machine learning and artificial intelligence is federated learning (FL), which allows multiple participants to contribute various training data to train a better model. It promises to keep the training data local for each participant, leading to low communication complexity and high privacy. However, there are still two problems in FL remain unsolved: (1) unable to handle vertically partitioned data, and (2) unable to support decision trees. Existing FL solutions for vertically partitioned data or decision trees require heavy cryptographic operations. In this paper, we propose a framework named FederBoost for private federated learning of gradient boosting decision trees (GBDT). It supports running GBDT over both horizontally and vertically partitioned data. The key observation for designing FederBoost is that the whole training process of GBDT relies on the order of the data instead of the values. Consequently, vertical FederBoost does not require any cryptographic operation and horizontal FederBoost only requires lightweight secure aggregation. We fully implement FederBoost and evaluate its utility and efficiency through extensive experiments performed on three public datasets. Our experimental results show that both vertical and horizontal FederBoost achieve the same level of AUC with centralized training where all data are collected in a central server; and both of them can finish training within half an hour even in WAN.Comment: 15 pages, 8 figure

Division Gets Better: Learning Brightness-Aware and Detail-Sensitive Representations for Low-Light Image Enhancement

Low-light image enhancement strives to improve the contrast, adjust the visibility, and restore the distortion in color and texture. Existing methods usually pay more attention to improving the visibility and contrast via increasing the lightness of low-light images, while disregarding the significance of color and texture restoration for high-quality images. Against above issue, we propose a novel luminance and chrominance dual branch network, termed LCDBNet, for low-light image enhancement, which divides low-light image enhancement into two sub-tasks, e.g., luminance adjustment and chrominance restoration. Specifically, LCDBNet is composed of two branches, namely luminance adjustment network (LAN) and chrominance restoration network (CRN). LAN takes responsibility for learning brightness-aware features leveraging long-range dependency and local attention correlation. While CRN concentrates on learning detail-sensitive features via multi-level wavelet decomposition. Finally, a fusion network is designed to blend their learned features to produce visually impressive images. Extensive experiments conducted on seven benchmark datasets validate the effectiveness of our proposed LCDBNet, and the results manifest that LCDBNet achieves superior performance in terms of multiple reference/non-reference quality evaluators compared to other state-of-the-art competitors. Our code and pretrained model will be available.Comment: 14 pages, 16 figure

A New Pixels Flipping Method for Huge Watermarking Capacity of the Invoice Font Image

Invoice printing just has two-color printing, so invoice font image can be seen as binary image. To embed watermarks into invoice image, the pixels need to be flipped. The more huge the watermark is, the more the pixels need to be flipped. We proposed a new pixels flipping method in invoice image for huge watermarking capacity. The pixels flipping method includes one novel interpolation method for binary image, one flippable pixels evaluation mechanism, and one denoising method based on gravity center and chaos degree. The proposed interpolation method ensures that the invoice image keeps features well after scaling. The flippable pixels evaluation mechanism ensures that the pixels keep better connectivity and smoothness and the pattern has highest structural similarity after flipping. The proposed denoising method makes invoice font image smoother and fiter for human vision. Experiments show that the proposed flipping method not only keeps the invoice font structure well but also improves watermarking capacity

Automated Meet-in-the-Middle Attack Goes to Feistel

Feistel network and its generalizations (GFN) are another important building blocks for constructing hash functions, e.g., Simpira v2, Areion, and the ISO standard Lesamnta-LW. The Meet-in-the-Middle (MitM) is a general paradigm to build preimage and collision attacks on hash functions, which has been automated in several papers. However, those automatic tools mostly focus on the hash function with Substitution-Permutation network (SPN) as building blocks, and only one for Feistel network by Schrottenloher and Stevens (at CRYPTO 2022). In this paper, we introduce a new automatic model for MitM attacks on Feistel networks by generalizing the traditional direct or indirect partial matching strategies and also Sasaki’s multi-round matching strategy. Besides, we find the equivalent transformations of Feistel and GFN can significantly simplify the MILP model. Based on our automatic model, we improve the preimage attacks on Feistel-SP-MMO, Simpira-2/-4-DM, Areion-256/-512-DM by 1-2 rounds or significantly reduce the complexities. Furthermore, we fill in the gap left by Schrottenloher and Stevens at CRYPTO 2022 on the large branch (b > 4) Simpira-b’s attack and propose the first 11-round attack on Simpira-6. Besides, we significantly improve the collision attack on the ISO standard hash Lesamnta-LW by increasing the attacked round number from previous 11 to ours 17 rounds

CipherGPT: Secure Two-Party GPT Inference

ChatGPT is recognized as a significant revolution in the field of artificial intelligence, but it raises serious concerns regarding user privacy, as the data submitted by users may contain sensitive information. Existing solutions for secure inference face significant challenges in supporting GPT-like models due to the enormous number of model parameters and complex activation functions. In this paper, we develop CipherGPT, the $\mathit{first}$ framework for secure two-party GPT inference, building upon a series of innovative protocols. First, we propose a secure matrix multiplication that is customized for GPT inference, achieving upto 2.5$\times$ speedup and 11.2$\times$ bandwidth reduction over SOTA. We also propose a novel protocol for securely computing GELU, surpassing SOTA by 4.2$\times$ in runtime, 3.4$\times$ in communication and 10.9$\times$ in precision. Furthermore, we come up with the first protocol for top-k sampling. We provide a full-fledged implementation and comprehensive benchmark for CipherGPT. In particular, we measure the runtime and communication for each individual operation, along with their corresponding proportions. We believe this can serve as a reference for future research in this area

Secure Transformer Inference Made Non-interactive

Secure transformer inference has emerged as a prominent research topic following the proliferation of ChatGPT. Existing solutions are typically interactive, involving substantial communication load and numerous interaction rounds between the client and the server. In this paper, we propose NEXUS the first non-interactive protocol for secure transformer inference, where the client is only required to submit an encrypted input and await the encrypted result from the server. Central to NEXUS are two innovative techniques: SIMD ciphertext compression/decompression, and SIMD slots folding. Consequently, our approach achieves a speedup of 2.8$\times$ and a remarkable bandwidth reduction of 368.6$\times$, compared to the state-of-the-art solution presented in S&P \u2724

GhABP19, a Novel Germin-Like Protein From Gossypium hirsutum, Plays an Important Role in the Regulation of Resistance to Verticillium and Fusarium Wilt Pathogens

Germin-like proteins (GLPs) are water-soluble plant glycoproteins belonging to the cupin superfamily. The important role of GLPs in plant responses against various abiotic and biotic stresses, especially pathogens, is well validated. However, little is known about cotton GLPs in relation to fungal pathogens. Here, a novel GLP gene was isolated from Gossypium hirsutum and designated as GhABP19. The expression of GhABP19 was upregulated in cotton plants inoculated with Verticillium dahliae and Fusarium oxysporum and in response to treatment with jasmonic acid (JA) but was suppressed in response to salicylic acid treatment. A relatively small transient increase in GhABP19 was seen in H2O2 treated samples. The three-dimensional structure prediction of the GhABP19 protein indicated that the protein has three histidine and one glutamate residues responsible for metal ion binding and superoxide dismutase (SOD) activity. Purified recombinant GhABP19 exhibits SOD activity and could inhibit growth of V. dahliae, F. oxysporum, Rhizoctonia solani, Botrytis cinerea, and Valsa mali in vitro. To further verify the role of GhABP19 in fungal resistance, GhABP19-overexpressing Arabidopsis plants and GhABP19-silenced cotton plants were developed. GhABP19-transgenic Arabidopsis lines showed much stronger resistance to V. dahliae and F. oxysporum infection than control (empty vector) plants did. On the contrary, silencing of GhABP19 in cotton conferred enhanced susceptibility to fungal pathogens, which resulted in necrosis and wilt on leaves and vascular discoloration in GhABP19-silenced cotton plants. The H2O2 content and endogenous SOD activity were affected by GhABP19 expression levels in Arabidopsis and cotton plants after inoculation with V. dahliae and F. oxysporum, respectively. Furthermore, GhABP19 overexpression or silencing resulted in activation or suppression of JA-mediated signaling, respectively. Thus, GhABP19 plays important roles in the regulation of resistance to verticillium and fusarium wilt in plants. These modulatory roles were exerted by its SOD activity and ability to activate the JA pathway. All results suggest that GhABP19 was involved in plant disease resistance