Generative Autoencoders as Watermark Attackers: Analyses of Vulnerabilities and Threats

Invisible watermarks safeguard images' copyrights by embedding hidden messages detectable by owners. It also prevents people from misusing images, especially those generated by AI models. Malicious adversaries can violate these rights by removing the watermarks. In order to remove watermarks without damaging the visual quality, the adversary needs to erase them while retaining the essential information in the image. This is analogous to the encoding and decoding process of generative autoencoders, especially variational autoencoders (VAEs) and diffusion models. We propose a framework using generative autoencoders to remove invisible watermarks and test it using VAEs and diffusions. Our results reveal that, even without specific training, off-the-shelf Stable Diffusion effectively removes most watermarks, surpassing all current attackers. The result underscores the vulnerabilities in existing watermarking schemes and calls for more robust methods for copyright protection

Provable Robust Watermarking for AI-Generated Text

We study the problem of watermarking large language models (LLMs) generated text -- one of the most promising approaches for addressing the safety challenges of LLM usage. In this paper, we propose a rigorous theoretical framework to quantify the effectiveness and robustness of LLM watermarks. We propose a robust and high-quality watermark method, Unigram-Watermark, by extending an existing approach with a simplified fixed grouping strategy. We prove that our watermark method enjoys guaranteed generation quality, correctness in watermark detection, and is robust against text editing and paraphrasing. Experiments on three varying LLMs and two datasets verify that our Unigram-Watermark achieves superior detection accuracy and comparable generation quality in perplexity, thus promoting the responsible use of LLMs. Code is available at https://github.com/XuandongZhao/Unigram-Watermark

An open framework for semantic code queries on heterogeneous repositories

To help developers understand and reuse programs, semantic queries on the source code itself is attractive. Although programs in heterogeneous languages are being controlled for collaborative software development, most queries supported by various source code repositories are based either on the metadata of the repositories, or on indexed identifiers and method signatures. Few provide full support to search for structures that are common across different programming languages and different viewpoints (hence heterogeneous). To facilitate understanding and reuses, in this paper, we propose a novel source code query framework that (1) transforms source code to a unified abstract syntax format, and handles heterogeneity (non-isomorphism) at the abstract syntax level; (2) stores source code on a cloud-based NoSQL storage in MongoDB; (3) rewrites semantic query patterns into the NoSQL form. The efficiency of the framework has been evaluated to support several open-source hosting platforms

Positive loop-closed automata: a decidable class of hybrid systems

AbstractThe model-checking problem for real-time and hybrid systems is very difficult, even for a well-formed class of hybrid systems—the class of linear hybrid automata—the problem is still undecidable in general. So an important question for the analysis and design of real-time and hybrid systems is the identification of subclasses of such systems and corresponding restricted classes of analysis problems that can be settled algorithmically. In this paper, we show that for a class of linear hybrid automata called positive loop-closed automata, the satisfaction problem for linear duration properties can be solved by linear programming. We extend the traditional regular expressions with duration constraints and use them as a language to describe the behaviour of this class of linear hybrid automata. The extended notation is called duration-constrained regular expressions. Based on this formalism, we show that the model-checking problem can be reduced formally to linear programs

Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging

Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a network (non-Euclidean) structure which is governed by its underlying graph patterns of pathological progression and metabolic connectivity. In order to effectively apply deep learning framework for PET image analysis to overcome its limitation on Euclidean grid, we develop a solution for 3D PET image representation and analysis under a generalized, graph-based CNN architecture (PETNet), which analyzes PET signals defined on a group-wise inferred graph structure. Computations in PETNet are defined in non-Euclidean, graph (network) domain, as it performs feature extraction by convolution operations on spectral-filtered signals on the graph and pooling operations based on hierarchical graph clustering. Effectiveness of the PETNet is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which shows improved performance over both deep learning and other machine learning-based methods.Comment: Jiaming Guo, Wei Qiu and Xiang Li contribute equally to this wor

Normal T1 relaxometry and extracellular volume of the pancreas in subjects with no pancreas disease: correlation with age and gender

OBJECTIVE Determine normal T1 and extracellular volume (ECV) of the pancreas in subjects with no pancreas disease and correlate with age and gender SUBJECTS AND METHODS We imaged 120 healthy subjects (age range: 20-78 years) who are on annual screening with MRI/MRCP for the possibility of pancreatic cancer. Subjects had a predisposition to develop pancreatic cancer, but no history of pancreas disease or acute symptoms. Equal number (n=60) of subjects were scanned on either 1.5 T or 3 T scanner using dual flip angle spoiled gradient echo technique incorporating fat suppression and correction for B1 field inhomogeneity. Optimization of imaging parameters were performed using a T1 phantom. ECV was calculated using pre- and post-contrast T1 of the pancreas and plasma. Regression analysis and Mann-Whitney tests were used for statistical analysis. RESULTS Median T1 on 1.5 T was 654 ms (IQR: 608-700); median T1 on 3 T was 717 ms (IQR: 582-850); median ECV on 1.5 T was 0.28 (IQR: 0.21-0.33) and median ECV on 3 T was 0.25 (IQR: 0.19-0.28). Age had a mild positive correlation with T1 (r= 0.24, p= 0.009), but not with ECV (r= 0.06, p=0.54). T1 and ECV were similar in both genders (p >0.05). CONCLUSION This study measured the median T1 and ECV of the pancreas in subjects with no pancreas disease. Pancreas shows longer T1 relaxation times in older population, whereas extracellular fraction remains unchanged. Median T1 values were different between two magnet strengths; however, no difference was seen between genders and ECV fractions