Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram modification.The most in JPEG bitstream consists of a sequence of VLCs(variable length codes)and the appended bits.Each VLC has a corresponding RLV(run/length value)to record the AC/DC coefficients.To achieve lossless data hiding with high payload,we shift the histogram of VLCs and modify the DHT segment to embed data.Since we sort the histogram of VLCs in descending order,the filesize expansion is limited.The paper’s key contribution includes:Lossless data hiding,less filesize expansion in identical pay-load and higher embedding efficiency.

Fine-Grained and Fair Identity Authentication Scheme for Mobile Networks Based on Blockchain

With the popularity of the internet,users hope to better protect their privacy while obtaining network services.However,in the traditional centralized authentication scheme,identity information such as the user's private key is generated,stored,and managed by the network operator.Users can't control their identity information,which will lead to a great threat to the privacy of users.Based on redactable blockchain,we propose a fine-grained and fair identity authentication scheme for mobile networks.In our proposed scheme,the user's identity information is generated and controlled by the users.We first propose a notion of score chameleon hash(SCH),which can delete or update the information of illegal users so as to dynamically update the status of users and provide users with more fine-grained and fair services.We propose another notion of self-updating secret sharing(SUSS),which allows users to update the trapdoor and the corresponding hash key after redacting the blockchain without requiring trusted authority to redistribute the trapdoor.Experimental results show that,compared with the immutable blockchain Bitcoin,the redactable blockchain in our identity authentication scheme provides users with fine-grained and fair redacting functions,and can be adopted with a small additional overhead.

Surface passivation using pyridinium iodide for highly efficient planar perovskite solar cells

Perovskite solar cells have developed rapidly in the past decades.However,there are large amounts of ionic defects at the surface and grain boundaries of perovskte films which are detrimental to both the efficiency and stability of perovskite solar cells.Here,an organic halide salt pyridinium iodide(PyI) is used in cation-anion-mixed perovskite for surface defect passivation.Different from the treatment with Lewis base pyridine(Py) which can only bind to the under-coordinated Pb ions,zwitterion molecule PyI can not only fill negative charged iodine vacancies,but also interact with positive charged defects.Compared with Py treatment,PyI treatment results in smoother surface,less defect densities and nonradiative recombination in perovskite,leading to an improved VOC, negligible J-V hysteresis and stable performance of devices.As a result,the champion PyI-treated planar perovskite solar cell with a high VOC of 1.187 V achieves an efficiency of 21.42%,which is higher than 20.37% of Py-treated device,while the pristine device without any treatment gets an efficiency of 18.83% at the same experiment conditions.

Robust Image Watermarking Based on Generative Adversarial Network

Digital watermark embeds information bits into digital cover such as images and videos to prove the creator’s ownership of his work.In this paper,we propose a robust image watermark algorithm based on a generative adversarial network.This model includes two modules,generator and adversary.Generator is mainly used to generate images embedded with watermark,and decode the image damaged by noise to obtain the watermark.Adversary is used to discriminate whether the image is embedded with watermark and damage the image by noise.Based on the model Hidden(hiding data with deep networks),we add a high-pass filter in front of the discriminator,making the watermark tend to be embedded in the mid-frequency region of the image.Since the human visual system pays more attention to the central area of the image,we give a higher weight to the image center region,and a lower weight to the edge region when calculating the loss between cover and embedded image.The watermarked image obtained by this scheme has a better visual performance.Experimental results show that the proposed architecture is more robust against noise interference compared with the state-of-art schemes.

Adaptive Backdoor Attack against Deep Neural Networks

In recent years,the number of parameters of deep neural networks(DNNs)has been increasing rapidly.The training of DNNs is typically computation-intensive.As a result,many users leverage cloud computing and outsource their training procedures.Outsourcing computation results in a potential risk called backdoor attack,in which a welltrained DNN would performabnormally on inputs with a certain trigger.Backdoor attacks can also be classified as attacks that exploit fake images.However,most backdoor attacks design a uniformtrigger for all images,which can be easilydetectedand removed.In this paper,we propose a novel adaptivebackdoor attack.We overcome this defect and design a generator to assign a unique trigger for each image depending on its texture.To achieve this goal,we use a texture complexitymetric to create a specialmask for eachimage,which forces the trigger tobe embedded into the rich texture regions.The trigger is distributed in texture regions,which makes it invisible to humans.Besides the stealthiness of triggers,we limit the range of modification of backdoor models to evade detection.Experiments show that our method is efficient in multiple datasets,and traditional detectors cannot reveal the existence of a backdoor.

Distortion Function for Emoji Image Steganography

Nowadays,emoji image is widely used in social networks.To achieve covert communication in emoji images,this paper proposes a distortion function for emoji images steganography.The profile of image content,the intra-and inter-frame correlation are taken into account in the proposed distortion function to fit the unique properties of emoji image.The three parts are combined together to measure the risks of detection due to the modification on the cover data.With the popular syndrome trellis coding(STC),the distortion of stego emoji image is minimized using the proposed distortion function.As a result,less detectable artifacts could be found in the stego images.Experimental results show that the proposed distortion function performs much higher undetectability than current state-of-the-art distortion function HILL which is designed for natural image.

Visible-light-induced decarboxylative alkynylation of carboxylic acids in batch and continuous flow

A green efficient photoredox-catalyzed decarboxylative alkynylation of carboxylic acids with alkynyl bromides has been developed.This broadly applicable protocol is presented whereinα-amino,aliphatic andα-oxy acids are converted into useful alkynylation products.The commercially-available organic photocatalyst 4CzIPN is used as the photocatalyst,organic base DBU is utilized as the base,and DMSO serves as solvent.This strategy features mild conditions,is metal-free,and is environmentally friendly.The batch and continuous-flow protocols described were applied to obtain a broader substrate scope of functionalization(more than 50 examples).Furthermore,we demonstrate that the use of microflow technology enhanced and intensified the reaction process,achieving significantly reduced reaction times(i.e.,10 min of residence time).

FTG: Score-based black-box watermarking by fragile trigger generation for deep model integrity verification

Deep neural networks(DNNs)are widely used in real-world applications,thanks to their exceptional performance in image recognition.However,their vulnerability to attacks,such as Trojan and data poison,can compromise the integrity and stability of DNN applications.Therefore,it is crucial to verify the integrity of DNN models to ensure their security.Previous research on model watermarking for integrity detection has encountered the issue of overexposure of model parameters during embedding and extraction of the watermark.To address this problem,we propose a novel score-based black-box DNN fragile watermarking framework called fragile trigger generation(FTG).The FTG framework only requires the prediction probability distribution of the final output of the classifier during the watermarking process.It generates different fragile samples as the trigger,based on the classification prediction probability of the target classifier and a specified prediction probability mask to watermark it.Different prediction probability masks can promote the generation of fragile samples in corresponding distribution types.The whole watermarking process does not affect the performance of the target classifier.When verifying the watermarking information,the FTG only needs to compare the prediction results of the model on the samples with the previous label.As a result,the required model parameter information is reduced,and the FTG only needs a few samples to detect slight modifications in the model.Experimental results demonstrate the effectiveness of our proposed method and show its superiority over related work.The FTG framework provides a robust solution for verifying the integrity of DNN models,and its effectiveness in detecting slight modifications makes it a valuable tool for ensuring the security and stability of DNN applications.