The proposed work builds upon the Random Forest machine learning algorithm to improve the process of digit forensic investigation in case of NFT. The following structure of this framework is aimed at identifying and disabling fraudulent or suspicious activities in NFT transactions by comparing different parameters like the Detection Time, False Positive Rate, the Total Transaction Volume Analyzed, the Anomalous Transaction Ratio, Clustering Accuracy, Data Utilization Efficiency, and Detection Sensitivity. Through using Random Forest, a solid ensemble learning technique that is well known for its on high accuracy as well as off overfitting tendency, it optimistically improves the identifying abilities of the framework in isolation of the false positives. The ability of the proposed system to deliver optimal results is further explained by line plots, area charts, histograms, and stem plots which all provide the variation of these metrics as the time proceeds. Not only does it enhance the effectiveness of detecting the fraudulent transactions, but it also enhances the application of data in the forensic analysis that creates a great advantage in the increasing realm of digital assets for investigators.
As digital crime continues to rise, the preservation of digital evidence has become a critical phase in digital forensic investigations. This phase focuses on securing and maintaining the integrity of evidence for legal proceedings. Existing solutions for evidence preservation, such as centralized storage systems and cloud frameworks, present challenges related to security and collaboration. In this paper, we propose a novel framework that addresses these challenges in the preservation phase of forensics. Our framework employs a combination of advanced technologies, including the following: (1) Segmenting evidence into smaller components for improved security and manageability, (2) Utilizing steganography for covert evidence preservation, and (3) Implementing blockchain to ensure the integrity and immutability of evidence. Additionally, we incorporate Long Short-Term Memory (LSTM) networks to enhance steganography in the evidence preservation process. This approach aims to provide a secure, scalable, and reliable solution for preserving digital evidence, contributing to the effectiveness of digital forensic investigations. An experiment using linguistic steganography showed that the LSTM autoencoder effectively generates coherent text from bit streams, with low perplexity and high accuracy. Our solution outperforms existing methods across multiple datasets, providing a secure and scalable approach for digital evidence preservation.
Open access
Advanced Steganography and Watermarking Techniques
Given a JPEG pipeline (compression or decompression), this paper demonstrates how to find the antecedent of an 8x8 block. If it exists, the block is considered compatible with the pipeline. For unaltered images, all blocks remain compatible with the original pipeline; however, for manipulated images, this is not necessarily true. This article provides a first demonstration of the potential of compatibility-based approaches for JPEG image forensics. It introduces a method to address the key challenge of finding a block antecedent in a high-dimensional space, relying on a local search algorithm with restrictions on the search space. We show that inpainting, copy-move, and splicing, when applied after JPEG compression, result in three distinct mismatch problems that can be detected. In particular, if the image is re-compressed after modification, the manipulation can be detected when the quality factor of the second compression is higher than that of the first. Through extensive experiments, we highlight the potential of this compatibility attack under varying degrees of assumptions. While our approach shows promising results-outperforming three state-of-the-art deep learning models in an idealized setting-it remains a proof of concept rather than an off-the-shelf forensic tool. Notably, with a perfect knowledge of the JPEG pipeline, our method guarantees zero false alarms in block-by-block localization, given sufficient computational power.
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Abstract In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F 1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named β risk . Since transactions with β risk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to contribute to the development of such studies.
Cryptocurrency markets are often characterized by market manipulation or, at the very least, by a sharp distinction between large and sophisticated investors and small retail investors.While traditional assets often see a divergence in the success of institutional traders and retail traders, we find an even more pronounced difference regarding the holders of Ethereum (ETH), the secondlargest cryptocurrency by volume.We see a significant difference in how large holders of ETH behave compared with smaller holders of ETH relative to price movements and the volatility of the cryptocurrency.We find that large ETH holders tend to increase their ETH holdings prior to a price increase, while small ETH holders tend to reduce their ETH holdings prior to a price increase.In other words, ETH returns tend to move in the direction that benefits crypto "whales" while reducing returns (or increasing loss) to "minnows."Additionally, we find that the volatility of ETH returns seems to be driven by small retail investors rather than by the crypto whales.Despite the advantages that DeFi (decentralized finance) trading networks offer, our findings provide evidence that many of the same challenges and vulnerabilities of TradFi (traditional finance) seem to remain within the ETH ecosystem, where larger, more sophisticated investors reap the benefits of their comparative advantages.
The domain of Digital Forensics for the Industrial Internet of Things (IIoT) and the proposed use of a Distributed Digital Ledger (DDL), has for the most part been theoretical in nature within the current literature. The work in this paper explores the practical feasibility of using DDL technology for Digital Forensics in the IIOT context. We detail a new methodology for testing the performance of writing to and reading from a DDL in an IIOT environment, and present findings on the overhead associated with storing and retrieving IIoT transactions in a DDL. We conclude that while it is possible to build and use a DDL for storing IIoT transactions, there are limitations to the number of sensors that can be supported by a single implementation and the time it takes to retrieve transactions may be too high to be practical for Digital Forensics.
The rapid proliferation of AI-generated âdeepfakeâ images, audio, and video is eroding public trust in digital media and amplifying risks to elections, markets, journalism, and personal safety. While AI detection models have improved, they face an adversarial âcat-and-mouseâ problem and often struggle to generalize across manipulation methods and compression regimes. This manuscript proposes and analyzes a hybrid, end-to-end approach that couples upstream provenance and authenticity signalsâanchored via open standards (e.g., C2PA Content Credentials) and decentralized ledgersâwith downstream AI detection and moderation. The pipeline captures and signs media at source; binds verifiable, tamper-evident metadata; anchors cryptographic hashes on a public or consortium blockchain; stores originals off-chain with content addressing (e.g., IPFS/Filecoin); and fuses these trust signals with model-based detectors and policy engines at distribution edges. We situate the proposal within current regulation (e.g., EU AI Act transparency duties) and state-of-the-art methods (e.g., watermarking such as SynthID, Stable Signature, and Tree-Ring; deepfake detectors trained on DFDC and FaceForensics++), highlighting both strengths and known attack vectors against watermarking that motivate layered defenses. A simulation-based evaluation illustrates that combining provenance signals with video-level transformer detectors can raise F1 from 0.85 to 0.92 while cutting false positives by ~41% in a balanced test set, primarily by rejecting credential-mismatched or hash-divergent media before expensive model inference. We further discuss privacy-preserving verification using W3C Verifiable Credentials (VC 2.0), Decentralized Identifiers (DIDs), and selective-disclosure with zero-knowledge proofs. The findings make a practical case for âtrust by designâ built on open standards, decentralized integrity proofs, and robust AI detection, implemented as a policy-aware defense-in-depth stack for platforms and newsrooms.
Arash Heidari, Nima Jafari Navimipour, Hasan DaÄ, Samira Talebi ¡ 5 authors
Abstract In recent years, the proliferation of deep learning (DL) techniques has given rise to a significant challenge in the form of deepfake videos, posing a grave threat to the authenticity of media content. With the rapid advancement of DL technology, the creation of convincingly realistic deepfake videos has become increasingly prevalent, raising serious concerns about the potential misuse of such content. Deepfakes have the potential to undermine trust in visual media, with implications for fields as diverse as journalism, entertainment, and security. This study presents an innovative solution by harnessing blockchain-based federated learning (FL) to address this issue, focusing on preserving data source anonymity. The approach combines the strengths of SegCaps and convolutional neural network (CNN) methods for improved image feature extraction, followed by capsule network (CN) training to enhance generalization. A novel data normalization technique is introduced to tackle data heterogeneity stemming from diverse global data sources. Moreover, transfer learning (TL) and preprocessing methods are deployed to elevate DL performance. These efforts culminate in collaborative global model training zfacilitated by blockchain and FL while maintaining the utmost confidentiality of data sources. The effectiveness of our methodology is rigorously tested and validated through extensive experiments. These experiments reveal a substantial improvement in accuracy, with an impressive average increase of 6.6% compared to six benchmark models. Furthermore, our approach demonstrates a 5.1% enhancement in the area under the curve (AUC) metric, underscoring its ability to outperform existing detection methods. These results substantiate the effectiveness of our proposed solution in countering the proliferation of deepfake content. In conclusion, our innovative approach represents a promising avenue for advancing deepfake detection. By leveraging existing data resources and the power of FL and blockchain technology, we address a critical need for media authenticity and security. As the threat of deepfake videos continues to grow, our comprehensive solution provides an effective means to protect the integrity and trustworthiness of visual media, with far-reaching implications for both industry and society. This work stands as a significant step toward countering the deepfake menace and preserving the authenticity of visual content in a rapidly evolving digital landscape.
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Advanced Steganography and Watermarking Techniques
Muhammad Iqbal, Arshad Iqbal, Abdullah Alshammari, Ihtisham Ali ¡ 6 authors
Decentralized cryptocurrencies like Bitcoin are digital assets with a price volatility nature, that allow for blockchain-based, peer-to-peer monetary transactions. Due to the price volatility problem with decentralized cryptocurrencies, research into the underlying pricing mechanism is required. Additionally, the behavior of Bitcoin prices is non-stationary, meaning that the statistical distribution of data varies over time. The proposed framework demonstrates the use of sophisticated machine learning models in predicting the short and medium-term trends and actual values of Bitcoin prices. This research goes beyond previous work that has only looked at machine learning-based categorization for a single day by instead using such models to forecast price changes seven, thirty, and ninety days into the future. The generated models are useful and work admirably, with the classification models reaching a maximum accuracy enhancement up to 31.48% for a 90-day prediction and a 11.76% F1-score for a forecast extending to the thirtieth day. In the case of regression the margin of error shifts from the one-time horizon for price projections to the next. A significant notable downfall occurs in different error metrics. These findings suggest that the models given here outperform those already found in the literature.
Open access
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
Zheyi Zhang, Yinghong Cao, Hadi Jahanshahi, Jun Mou
With the rapid development of blockchain technology, the security of non-fungible tokens (NFT) in the transaction process has attracted much attention. In the transaction process, the commoditized NFT images will inevitably involve display and leakage problems, which is likely to lead to economic losses drink copyright disputes between the trading parties. To protect transaction security, a chaotic color multi-image compression encryption/LSB data-type steganography scheme is proposed in the paper. A series of chaotic sequences are obtained by iterating the chaotic map, while compression sensing (CS) is introduced to compress multiple secret images and fuse the compressed secret images into one large secret image. Then this image is encrypted, and the encrypted large secret image is hidden on multiple cover images by steganography. This encryption can hide the change in the statistical properties caused by steganography. Finally, the cover image is combined with the 3D object model by using it as a texture for the 3D object model to realize the type steganography of the image data. The simulation and performance test results of the scheme illustrate that the scheme has a large enough key space and steganography capacity, as well as good resistance to differential attacks, statistical attacks, compression reconstruction quality, and robustness. This scheme can well protect the transaction security of NFT images, and at the same time open a channel connecting 2D images and 3D obj textured models, which provides a new idea for steganography.
Open access
Advanced Steganography and Watermarking Techniques
Kar Balan, Andrew Gilbert, Alexander Black, Simon Jenni ¡ 6 authors
We present DECORAIT; a decentralized registry through which content creators may assert their right to opt in or out of AI training as well as receive reward for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amounts of data scraped from public sources. Model and content creators who may wish to share their work openly without sanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data in order to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA (Coalition for Content Provenance and Authenticity) standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.
Digital forensics, a crucial subset of cybersecurity, encompasses sophisticated tools and methodologies for the interpretation, analysis, and investigation of digital evidence, facilitating the identification and mitigation of cybercrimes and security breaches. With the advent of cryptocurrencies, an array of unique challenges has emerged in the domain of digital forensic investigations. This review elucidates the prevailing state of digital forensic practices vis-Ă -vis cryptocurrencies, emphasizing the obstacles and limitations inherent in probing decentralized and intricate technologies. Notable deficiencies in extant investigative practices were observed. Solutions proffered encompass the formulation of novel software applications tailored for cryptocurrency analyses, the integration of machine learning and artificial intelligence capabilities, and the employment of advanced analytics to discern patterns and irregularities within blockchain transactions. Furthermore, a pioneering methodology, merging traditional digital forensic strategies with blockchain-specific techniques, is posited for efficacious cryptocurrency inquiries. The analysis underscores the imperative for a renewed paradigm in digital forensic examinations to surmount the challenges integral to cryptocurrency probes. By forging novel methodologies and standardizing investigative procedures, support for legal enforcement endeavors can be enhanced, facilitating the efficacious detection and prosecution of cryptocurrency-associated misdemeanors.
Blockchain technology has brought notable advancements to diverse industries. The introduction of non-fungible tokens (NFTs) has particularly led to a lucrative market for unique digital asset ownership verification, including digital artworks. However, this trend has also given rise to concerns such as fraud, stolen works, authenticity, and copyright issues. Illicit traders exploit the market by trading unauthorized copies of digital objects as NFTs. In this study, we propose the use of digital watermarking as a means to establish the authenticity of NFTs and enhance the marketplaceâs credibility.
Open access
Advanced Steganography and Watermarking Techniques
Frederik Temmermans, Sabrina Caldwell, Symeon Papadopoulos, Fernando Pereira ¡ 5 authors
Advances in media content manipulation and artificially generated content pose new challenges to the assessment of media authenticity. While automated detection methods can provide meaningful insights and decision support in some scenarios, they cannot provide trustworthy and comprehensive information about the origin and provenance of media assets. Therefore, a longer-term approach should rather focus on secure and interoperable annotations related to the creation and provenance of media. In October 2020, the JPEG Committee initiated a standardization exploration named "JPEG Fake Media" to address these needs. Subsequently, since many of the requirements, for example related to secure annotation and identification of media assets, are also relevant to achieve interoperability in Non-Fungible Tokens (NFTs) an additional exploration was initiated, specifically focused on standardization needs for NFTs. In April 2022 a first Call for Proposals on JPEG Fake Media was issued. Based on the responses to the call, a new standardization project named JPEG Trust was initiated to specify an interoperable framework for establishing trust in media production, distribution, and consumption. This paper presents the journey of JPEG to leverage formal methods of standardization in this context, starting from the initial JPEG Fake Media exploration, followed by the subsequent consideration of NFT use cases and requirements, through to the commencement of the new JPEG Trust international standard.
Open access
2 source records
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
A generative adversarial network is a deep learning model, an unsupervised learning method. In computer vision, the generative adversarial network is a research direction with rapid development in recent years; Similarly, the rise of cryptocurrency Non-Fungible Tokens (NFT) in recent years has also attracted much attention to the field of art. As an "irreplaceable currency" NFT provides a more novel and convenient way for content creators and artists to create and increases the continuous income of original creators. At the same time, it has also attracted widespread attention to the financial field. Therefore, this paper is determined to combine the generative adversarial network of the production of NFT and discuss and analyze the autonomous computer generation of artworks. Firstly, this paper starts with the model's structure, the design of the objective function, Block chain technology, and Irreplaceable tokens encrypted using blockchain technology. Then, the image generated by the whole generative adversarial network and transformed into NFT works are described in detail. In addition, this paper briefly discusses the development ethics of human art and machine art and the prospects for its development trend.
Open access
Generative Adversarial Networks and Image Synthesis
Blockchain technology is used to support digital assets such as cryptocurrencies and tokens. Commonly, smart contracts are used to generate tokens on top of the blockchain network. There are two fundamental types of tokens: fungible and non-fungible (NFTs). This paper focuses on NFTs and offers a technique to spot plagiarism in NFT images. NFTs are information that is appended to files to produce distinctive signatures. It can be found in image files, real artifacts, literature published online, and various other digital media. Plagiarism and fraudulent NFT images are becoming a big concern for artists and customers. This paper proposes an efficient deep learning-based approach for NFT image plagiarism detection using the EfficientNet-B0 architecture and the Triplet Semi-Hard Loss function. We trained our model using a dataset of NFT images and evaluated its performance using several metrics, including loss and accuracy. The results showed that the EfficientNet-B0-based deep neural network with triplet semi-hard loss outperformed other models such as Resnet50, DenseNet, and MobileNetV2 in detecting plagiarized NFTs. The experimental results demonstrate sufficient to be implemented in various NFT marketplaces.
Open access
Digital Media Forensic Detection
Generative Adversarial Networks and Image Synthesis
Advanced Steganography and Watermarking Techniques
There has been a lot of hype around blockchains and non fungible tokens (NFTs). However the technology is yet to establish itself beyond a limited number of fairly specific use cases, namely cryptocurrency and digital art. This commentary seeks to propose an outline framework for NFTs and Blockchains for digital provenance of important digital artifacts such as citizen intelligence and newspapers. This article develops an outline framework and reasoning for NFT and Blockchain technology to be used to establish provenance of digital artifacts in what is an increasingly contested digital space. A space where the subtle (and perhaps not so subtle) manipulation of video, images, and all forms of digital evidence and documents by humans, and increasingly artificial intelligence, could be used to challenge contemporary narratives and manipulate the past. This framework should be seen as an opening proposal to ignite discussion, as the establishment of any technology in this space should not be done without careful consideration. Nor should it be done by a single individual or group.
With the growing popularity of smartphone photography in recent years, web photos play an increasingly important role in all walks of life. Source camera identification of web photos aims to establish a reliable linkage from the captured images to their source cameras, and has a broad range of applications, such as image copyright protection, user authentication, investigated evidence verification, etc. This paper presents an innovative and practical source identification framework that employs neural-network enhanced sensor pattern noise to trace back web photos efficiently while ensuring security. Our proposed framework consists of three main stages: initial device fingerprint registration, fingerprint extraction and cryptographic connection establishment while taking photos, and connection verification between photos and source devices. By incorporating metric learning and frequency consistency into the deep network design, our proposed fingerprint extraction algorithm achieves state-of-the-art performance on modern smartphone photos for reliable source identification. Meanwhile, we also propose several optimization sub-modules to prevent fingerprint leakage and improve accuracy and efficiency. Finally for practical system design, two cryptographic schemes are introduced to reliably identify the correlation between registered fingerprint and verified photo fingerprint, i.e. fuzzy extractor and zero-knowledge proof (ZKP). The codes for fingerprint extraction network and benchmark dataset with modern smartphone cameras photos are all publicly available at https://github.com/PhotoNecf/PhotoNecf 1.
Open access
3 source records
cs.CV
Digital Media Forensic Detection
Advanced Steganography and Watermarking Techniques
Cryptocurrencies have become a global phenomenon and its trading volume has been increasing since 2017 Aloosh and Li (2019). However, cryptocurrencies have been accused of market manipulation in the past. Benfordâs law is widely used for detecting probability of frauds and manipulation in various fields. This study applied Benfordâs law on trade volume of cryptocurrencies. Chi- square statistics revealed that except for Cardano and USDT all the other cryptocurrencies did not conform to the distribution and reveals the dataset could have been manipulated. This method may be used as the pre-requisite before doing fine-grain screening such as machine learning and graph-based searching.
With the recent proliferation of blockchains, identifying security risks to them has become an important issue. Among the various types of cyberattacks against blockchains, the blockchain poisoning attack involves the storing of malicious data in the blockchain to compromise it. One scenario is an attack that distributes forgeries of digital content traded and managed using Non-Fungible Token (NFT) on the blockchain. Currently, concomitant with the growing interest in NFT-based content trading, blockchain poisoning attacks on NFT trading and their effects have also increased. In this study, we examined the issues that may lead to attacks in the process from generation to distribution of digital content using NFT from the viewpoint of flexibility and interoperability of the content. Consequently, we discovered that there are two types of attack risks in NFT trading using malicious content: fake attacks and reuse attacks. As a countermeasure against these attacks, we propose a method for verifying the authenticity of the content itself using a decentralized scheme. The proposed method ensures the confidentiality of contents by using deep learning as an irreversible transformation operation in the distributed scheme and for privacy protection.
Open access
Advanced Steganography and Watermarking Techniques
Wei Yao, Jingyi Gu, Wenlu Du, Fadi P. Deek ¡ 5 authors
The increasing popularity of crypto assets has resulted in greater cryptocurrency investor interest and more exposure in both industry and academia. Despite the substantial socioeconomic benefits, the anonymous character of cryptocurrency trading makes it prone to abuse and a magnet for illicit purposes, which cause monetary losses for individual traders and erosion in the standing of the tokenomics industry. To regulate the illicit behavior and secure users' privacy for cryptocurrency trading, we present an Anomaly Detection and Privacy-Preserving (ADPP) Framework integrating blockchain and deep learning technologies. Specifically, ADPP leverages blockchain technologies to build a user management platform that ensures anonymity and enhances the privacy-preservation of user information. Atop the user management system, an Anomaly Detection System adapts neural networks and imbalanced learning on topological cryptocurrency flow among users to identify anomalous addresses and maintain a sanction list repository. The experiments on the real-world dataset demonstrate the effectiveness and superior performance of ADPP. The flexible framework can be easily generalized to the crypto assets with public real-time transaction (e.g., Non-fungible Token), which takes up a significant proportion of market capitalization in the domain of tokenomics.
The recent creation of Non Fungible Tokens (NFTs) has enabled a multibillionaire market for digital artistic works including images or sequence of images, videos, and animated gifs. With this new trend issues regarding fraud, stolen works, authenticity, and copyright came along. The goal of this chapter is to provide an overview of the watermarking techniques that can be employed to mitigate those issues. We will discuss transparency, robustness, and payload of watermarking techniques aiming to educate the artists, researchers, and developers about the many approaches that watermarking techniques provide and the resulting trade-offs. We focus on fragile watermarking techniques due to their high transparency for embedding into artistic works. We discuss the spread spectrum and Least Significant Bit techniques. We describe the usual process of NFT minting into a blockchain and propose a more secure certification protocol with watermarking which employs the same usual NFT minting offered by current marketplaces. The proposed certification protocol mints a checksum string into a blockchain, ensuring the validity of the watermark and the information embedded into this watermark. This proposed protocol validates the date of creation and author identification which are transparently embedded in the artistic work, thus, increasing the security and confidence of markets for artistic works transactions.
Open access
Advanced Steganography and Watermarking Techniques
The data in the blockchain cannot be tampered with and the users are anonymous, which enables the blockchain to be a natural carrier for covert communication. However, the existing methods of covert communication in blockchain suffer from the predefined channel structure, the capacity of a single transaction is not high, and the fixed transaction behaviors will lower the concealment of the communication channel. Therefore, this paper proposes a derivation matrix-based covert communication method in blockchain. It uses dual-key to derive two types of blockchain addresses and then constructs an address matrix by dividing addresses into multiple layers to make full use of the redundancy of addresses. Subsequently, to solve the problem of the lack of concealment caused by the fixed transaction behaviors, divide the rectangular matrix into square blocks with overlapping regions and then encrypt different blocks sequentially to make the transaction behaviors of the channel addresses match better with those of the real addresses. Further, the linear congruence algorithm is used to generate random sequence, which provides a random order for blocks encryption, and thus enhances the security of the encryption algorithm. Experimental results show that this method can effectively reduce the abnormal transaction behaviors of addresses while ensuring the channel transmission efficiency.
Open access
Advanced Steganography and Watermarking Techniques
Daniel Kang, Tatsunori Hashimoto, Ion Stoica, Yi Sun
Over the past few years, AI methods of generating images have been increasing in capabilities, with recent breakthroughs enabling high-resolution, photorealistic "deepfakes" (artificially generated images with the purpose of misinformation or harm). The rise of deepfakes has potential for social disruption. Recent work has proposed using ZK-SNARKs (zero-knowledge succinct non-interactive argument of knowledge) and attested cameras to verify that images were taken by a camera. ZK-SNARKs allow verification of image transformations non-interactively (i.e., post-hoc) with only standard cryptographic hardness assumptions. Unfortunately, this work does not preserve input privacy, is impractically slow (working only on 128$\times$128 images), and/or requires custom cryptographic arguments. To address these issues, we present zk-img, a library for attesting to image transformations while hiding the pre-transformed image. zk-img allows application developers to specify high level image transformations. Then, zk-img will transparently compile these specifications to ZK-SNARKs. To hide the input or output images, zk-img will compute the hash of the images inside the ZK-SNARK. We further propose methods of chaining image transformations securely and privately, which allows for arbitrarily many transformations. By combining these optimizations, zk-img is the first system to be able to transform HD images on commodity hardware, securely and privately.
Open access
2 source records
Digital Media Forensic Detection
Adversarial Robustness in Machine Learning
Generative Adversarial Networks and Image Synthesis