A Somewhat Robust Image Watermark against Diffusion-based Editing Models

Recently, diffusion models (DMs) have become the state-of-the-art method for image synthesis. Editing models based on DMs, known for their high fidelity and precision, have inadvertently introduced new challenges related to image copyright infringement and malicious editing. Our work is the first to formalize and address this issue. After assessing and attempting to enhance traditional image watermarking techniques, we recognize their limitations in this emerging context. In response, we develop a novel technique, RIW (Robust Invisible Watermarking), to embed invisible watermarks leveraging adversarial example techniques. Our technique ensures a high extraction accuracy of $96\%$ for the invisible watermark after editing, compared to the $0\%$ offered by conventional methods. We provide access to our code at https://github.com/BennyTMT/RIW

Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System

In recent years, the security issues of artificial intelligence have become increasingly prominent due to the rapid development of deep learning research and applications. Backdoor attack is an attack targeting the vulnerability of deep learning models, where hidden backdoors are activated by triggers embedded by the attacker, thereby outputting malicious predictions that may not align with the intended output for a given input. In this work, we propose a novel black-box backdoor attack based on machine unlearning. The attacker first augments the training set with carefully designed samples, including poison and mitigation data, to train a `benign' model. Then, the attacker posts unlearning requests for the mitigation samples to remove the impact of relevant data on the model, gradually activating the hidden backdoor. Since backdoors are implanted during the iterative unlearning process, it significantly increases the computational overhead of existing defense methods for backdoor detection or mitigation. To address this new security threat, we proposes two methods for detecting or mitigating such malicious unlearning requests. We conduct the experiment in both exact unlearning and approximate unlearning (i.e., SISA) settings. Experimental results indicate that: 1) our attack approach can successfully implant backdoor into the model, and sharding increases the difficult of attack; 2) our detection algorithms are effective in identifying the mitigation samples, while sharding reduces the effectiveness of our detection algorithms

Functional Connectivity Density, Local Brain Spontaneous Activity, and Their Coupling Strengths in Patients With Borderline Personality Disorder

In this study, combining degree centrality (DC) and fractional amplitude of low frequency fluctuation (fALFF) analyses of resting state (rs)-functional magnetic resonance imaging (fMRI) data, we aimed to explore functional connectivity density, local brain spontaneous activity, and their coupling strengths in borderline personality disorder (BPD). Forty-three BPD patients and 39 demographically-matched controls underwent rs-fMRI after completing a series of psychological tests. Two-sample t-tests were performed to compare DC and fALFF between these two groups. Across-voxel correlation analysis was conducted to assess DC-fALFF coupling strengths in each group. Imaging parameters and psychological variables were correlated by Pearson correlation analysis in the BPD group. Altered DC and fALFF values in the BPD group, compared with the control group, were distributed mainly in default mode network (DMN), and DC-fALFF coupling strengths were decreased in the left middle temporal gyrus (MTG) and right precuneus in the BPD group. Additionally, insecure attachment scores correlated positively with left precuneus DC and negatively with fALFF of the right posterior cingulate cortex (PCC) in the BPD group. These altered DC and fALFF findings indicate that the BPD patients had disturbed functional connectivity density and local spontaneous activity in the DMN compared with control subjects. Their decreased connectivity-amplitude coupling suggests that the left MTG and right precuneus may be functional impairment hubs in BPD. Disturbed rs function in the left precuneus and right PCC might underlie insecure attachment in BPD

Resting-State Default Mode Network Related Functional Connectivity Is Associated With Sustained Attention Deficits in Schizophrenia and Obsessive-Compulsive Disorder

Background: Previous studies have indicated the resting-state default mode network (DMN) related connectivity serving as predictor of sustained attention performance in healthy people. Interestingly, sustained attention deficits as well as DMN-involved functional connectivity (FC) alterations are common in both patients with schizophrenia (SCZ) and with obsessive-compulsive disorder (OCD). Thus, the present study was designed to investigate whether the DMN related resting-state connectivity alterations in these two psychiatric disorders were neural correlates of their sustained attention impairments.Methods: The study included 17 SCZ patients, 35 OCD patients and 36 healthy controls (HCs). Sustained attention to response task was adopted to assess the sustained attention. Resting-state scan was administrated and seed-based whole-brain FC analyses were performed with seeds located in classical DMN regions including bilateral medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC).Results: Both SCZ and OCD patients had poorer sustained attention than HCs. Sustained attention deficits in OCD was negatively correlated with their impaired FC of right mPFC-left superior frontal gyrus (SFG) within DMN, and that in SCZ was significantly correlated with their altered FC of left mPFC-bilateral anterior cingulate cortex (ACC) which indicated interaction between DMN and salience network. In addition, the FC between left mPFC and right parietal lobe indicating the interaction between DMN and frontal-parietal network was correlated with sustained attention in both SCZ and OCD.Conclusion: These findings suggest the importance of DMN-involved connectivity, both within and between networks in underlying sustained attention deficits in OCD and SCZ. Results further support the potential of resting-state FC in complementing information for cognitive deficits in psychiatric disorders

A survey of practical adversarial example attacks

Abstract Adversarial examples revealed the weakness of machine learning techniques in terms of robustness, which moreover inspired adversaries to make use of the weakness to attack systems employing machine learning. Existing researches covered the methodologies of adversarial example generation, the root reason of the existence of adversarial examples, and some defense schemes. However practical attack against real world systems did not appear until recent, mainly because of the difficulty in injecting a artificially generated example into the model behind the hosting system without breaking the integrity. Recent case study works against face recognition systems and road sign recognition systems finally abridged the gap between theoretical adversarial example generation methodologies and practical attack schemes against real systems. To guide future research in defending adversarial examples in the real world, we formalize the threat model for practical attacks with adversarial examples, and also analyze the restrictions and key procedures for launching real world adversarial example attacks

High-Temperature Oxidation Behaviors of AlCrTiSi_0.2 High-Entropy Alloy Doped with Rare Earth La and Y

High-entropy alloys (HEAs) were prepared with strong antioxidant metals Al, Cr, Ti, and Si as matrix elements, and the effects of rare earth (RE) lanthanum (La) and yttrium (Y) doping on their microstructures and high-temperature oxidation resistance were explored in this study. The AlCrTiSi0.2RE0.02 HEAs were prepared by using vacuum arc melting and were oxidized mass gain at 1000 °C. After oxidation for 53 h, AlCrTiSi0.2 HEA had a mass increase of 1.195 mg/cm2, and it had the best oxidation resistance of three HEAs (AlCrTiSi0.2, AlCrTiSi0.2La0.02, and AlCrTiSi0.2Y0.02). The surface oxide layers of three HEAs mainly consisted of Al and Ti oxides; the layered oxide film of AlCrTiSi0.2 alloy was mainly composed of dense Al2O3, and the acicular oxide films of AlCrTiSi0.2La0.02 and AlCrTiSi0.2Y0.02 alloys were primarily composed of loose Ti oxide. Doping La and Y decreased the oxidation resistance of AlCrTiSi0.2. In the early stage of oxidation of rare earth HEAs, the surface oxide layer was loose because La and Y reacted with the matrix metal, which slowed down the diffusion of element Al or accelerated the diffusion of element Ti. In the late stage of oxidation, La and Y interacted with O and entered the matrix metal to form rare earth oxides

Variation of microstructures and properties of Co0.2CrAlNi high entropy alloy doped Si

The effects of Si on the microstructures and various properties of Co0.2CrAlSixNi (in mole ratio, x = 0, 0.1, 0.3, 0.5, and 0.7) multi-component high entropy alloys were investigated. Co0.2CrAlNi was selected as the matrix, and Si was doped as the reinforcement. Microstructure analysis revealed that the Co0.2CrAlSixNi high entropy alloys consisted of two body-centered cubic (BCC) phases, the Cr-rich phase and nano-scale Ni-Al phase. With the addition of Si, the new Cr3Si phase was precipitated, and the crystallite size decreased to some extent in the alloys. Hardness test indicated that the alloys had high hardness with the maximum value of 973 at x = 0.5. The crystallite size and Cr3Si phase content were two key factors significantly affecting the hardness of Co0.2CrAlSixNi HEAs. Electrochemical test results indicated that Si affected the formation of the passivation film, the corrosion potential of Co0.2CrAlSi0.1Ni was - 0.37 V and the self -corrosion current density was 1.15 x 10-6 A/cm2, respectively. The main contribution in which Si doping affected the corrosion resistance of HEA was reduced oxidized Cr3+ and Ni2+ on the alloy surface. This study is of great significance to develop light and hard engineering materials for practical applications.(c) 2022 Elsevier B.V. All rights reserved

Variation of microstructures and properties of Co0.2CrAlNi high entropy alloy doped Si

The effects of Si on the microstructures and various properties of Co0.2CrAlSixNi (in mole ratio, x = 0, 0.1, 0.3, 0.5, and 0.7) multi-component high entropy alloys were investigated. Co0.2CrAlNi was selected as the matrix, and Si was doped as the reinforcement. Microstructure analysis revealed that the Co0.2CrAlSixNi high entropy alloys consisted of two body-centered cubic (BCC) phases, the Cr-rich phase and nano-scale Ni-Al phase. With the addition of Si, the new Cr3Si phase was precipitated, and the crystallite size decreased to some extent in the alloys. Hardness test indicated that the alloys had high hardness with the maximum value of 973 at x = 0.5. The crystallite size and Cr3Si phase content were two key factors significantly affecting the hardness of Co0.2CrAlSixNi HEAs. Electrochemical test results indicated that Si affected the formation of the passivation film, the corrosion potential of Co0.2CrAlSi0.1Ni was - 0.37 V and the self -corrosion current density was 1.15 x 10-6 A/cm2, respectively. The main contribution in which Si doping affected the corrosion resistance of HEA was reduced oxidized Cr3+ and Ni2+ on the alloy surface. This study is of great significance to develop light and hard engineering materials for practical applications.(c) 2022 Elsevier B.V. All rights reserved

A Voxel-Based Morphometric MRI Study in Young Adults with Borderline Personality Disorder.

BACKGROUND:Increasing evidence has documented subtle changes in brain morphology and function in patients with borderline personality disorder (BPD). However, results of magnetic resonance imaging volumetry in patients with BPD are inconsistent. In addition, few researchers using voxel-based morphometry (VBM) have focused on attachment and childhood trauma in BPD. This preliminary study was performed to investigate structural brain changes and their relationships to attachment and childhood trauma in a homogenous sample of young adults with BPD. METHOD:We examined 34 young adults with BPD and 34 healthy controls (HCs) to assess regionally specific differences in gray matter volume (GMV) and gray matter concentration (GMC). Multiple regressions between brain volumes measured by VBM and attachment style questionnaire (ASQ) and childhood trauma questionnaire (CTQ) scores were performed. RESULTS:Compared with HCs, subjects with BPD showed significant bilateral increases in GMV in the middle cingulate cortex (MCC)/posterior cingulate cortex (PCC)/precuneus. GMC did not differ significantly between groups. In multiple regression models, ASQ insecure attachment scores were correlated negatively with GMV in the precuneus/MCC and middle occipital gyrus in HCs, HCs with more severe insecure attachment showed smaller volumes in precuneus/MCC and middle occipital gyrus, whereas no negative correlations between insecure attachment and GMV in any region were found in BPD group. In addition, CTQ total scores were not correlated with GMV in any region in the two groups respectively. CONCLUSIONS:Our findings fit with those of previous reports of larger precuneus GMV in patients with BPD, and suggest that GMV in the precuneus/MCC and middle occipital gyrus is associated inversely with insecure attachment style in HCs. Our finding of increased GMV in the MCC and PCC in patients with BPD compared with HCs has not been reported in previous VBM studies