Image manipulation localization (IML) is a critical technique in media forensics, focusing on identifying tampered regions within manipulated images. Most existing IML methods require extensive training on labeled datasets with both image-level and pixel-level annotations. These methods often struggle with new manipulation types and exhibit low generalizability. In this work, we propose a training-free IML approach using diffusion models. Our method adaptively selects an appropriate number of diffusion timesteps for each input image in the forward process and performs both conditional and unconditional reconstructions in the backward process without relying on external conditions. By comparing these reconstructions, we generate a localization map highlighting regions of manipulation based on inconsistencies. Extensive experiments were conducted using sixteen state-of-the-art (SoTA) methods across six IML datasets. The results demonstrate that our training-free method outperforms SoTA unsupervised and weakly-supervised techniques. Furthermore, our method competes effectively against fully-supervised methods on novel (unseen) manipulation types.

We investigate how to characterize semantic significance (SS) in detecting image manipulations (IMD) for media forensics. We introduce the Characterization of Seman-tic Impact for IMD (CSI-IMD) dataset, which focuses on localizing and evaluating the semantic impact of image manipulations to counter advanced generative techniques. Our evaluation of 10 state-of-the-art IMD and localization methods on CSI-IMD reveals key insights. Unlike existing datasets, CSI-IMD provides detailed semantic annotations beyond traditional manipulation masks, aiding in the development of new defensive strategies. The dataset features manipulations from advanced generation methods, offering various levels of semantic significance. It is divided into two parts: a gold-standard set of 1,000 manu-ally annotated manipulations with high-quality control, and an extended set of 500,000 automated manipulations for large-scale training and analysis. We also propose a new SS-focused task to assess the impact of semantically targeted manipulations. Our experiments show that current IMD methods struggle with manipulations created using stable diffusion, with TruFor and Cat-Net performing the best among those tested. The CSI-IMD dataset will become available at https://github.com/csiimd/csiimd.

Recent studies show that inaudible attacks pose a non-negligible security risk to smart speakers. While several countermeasures have been proposed to detect the occurrence of the inaudible attack passively, accurately locating the attack source in 3D free space remains an unresolved challenge. Arrow is designed to bridge this gap by attempting to detect the occurrence of inaudible attacks and determine their localization simultaneously. Instead of relying on dedicated hardware components, Arrow is implemented with the microphone array widely deployed on COTS (Commercial Off-The-Shelf) smart speakers. Throughout the spatial information captured by the microphone array, Arrow establishes a spatial mapping model and derives orientation-related features to pinpoint the location of the attack source. Furthermore, to improve the robustness against co-channel interference, Arrow adopt carefully-modulated ultrasonic waveforms to achieve noise-robust attack detection. Through the above technical mechanism, Arrow can significantly improve the security level of voice assistants on smart speakers with nearly zero deployment cost. We implement a prototype of Arrow and conduct a comprehensive performance evaluation. The results show Arrow can achieve 2.5 ○ and 7 ○ error in DoA estimation for horizontal and vertical angles, respectively.

The ability to detect manipulation in multimedia data is vital in digital forensics. Existing Image Manipulation Detection (IMD) methods are mainly based on detecting anomalous features arisen from image editing or double compression artifacts. All existing IMD techniques encounter challenges when it comes to detecting small tampered regions from a large image. Moreover, compression-based IMD approaches face difficulties in cases of double compression of identical quality factors. To investigate the State-of-The-Art (SoTA) IMD methods in those challenging conditions, we introduce a new Challenging Image Manipulation Detection (CIMD) benchmark dataset, which consists of two subsets, for evaluating editing-based and compression-based IMD methods, respectively. The dataset images were manually taken and tampered with high-quality annotations. In addition, we propose a new two-branch network model based on HRNet that can better detect both the image-editing and compression artifacts in those challenging conditions. Extensive experiments on the CIMD benchmark show that our model significantly outperforms SoTA IMD methods on CIMD. The dataset is available at: https://github.com/ZhenfeiZ/CIMD.

Abstract Stealth address protocol (SAP) is widely used in blockchain to achieve anonymity. In this paper, we formalize a key derivable signature scheme (KDS) to capture the functionality and security requirements of SAP. We then propose a framework to construct key separation KDS, which follows the key separation principle as all existing SAP solutions to avoid the reuse of the master keys in the derivation and signature component. We also study the joint security in KDS and construct a key reusing KDS framework, which implies the first compact stealth address protocol using a single key pair. Finally, we provide instantiations based on the elliptic curve (widely used in cryptocurrencies) and on the lattice (with quantum resistance), respectively.

Multi-signatures allow for compressing many signatures for the same message that were generated under independent keys into one small aggregated signature. This primitive is particularly useful for proof-of-stake blockchains, like Ethereum, where the same block is signed by many signers, who vouch for the block's validity. Being able to compress all signatures for the same block into a short string significantly reduces the on-chain storage costs, which is an important efficiency metric for blockchains.

Recent works have shown that inaudible signals (at ultrasound frequencies) can become audible to the microphone by exploiting the nonlinear effects. With a well-designed inaudible signal, an adversary can control Amazon Echo and Google Homelike devices in people’s rooms silently and remotely. A voice command like “Alexa, open the door“ can be a serious treat. Although recent works design various methods against such inaudible attacks, one important issue remains open: there is no clear solution to locate the attack source accurately. Obviously, the only way to completely eliminate such inaudible threats is to locate and remove the attack source. This paper is an attempt to close this gap. We propose Arrow, an effective method to help users locate the ultrasound attack source in 3D space indoors. Arrow establishes the relationship between inaudible signals and the recorded sounds of the microphone, and then explores the architecture of the embedded microphone array on smart speaker for extracting a 3D direction-specific signature. By learning such directional signature, Arrow can accurately estimate the spatial orientation of the inaudible attack source and help users to locate and remove it. We implement a prototype of Arrow and conduct comprehensive experiments to validate its performance. The results show Arrow can achieve 2.5° and 7° error in DoA(Direction of Arrival) estimation for horizontal and vertical angles, respectively.

The focus of this work are multi-signatures schemes in the synchronized setting. A multi-signature scheme allows multiple signatures for the same message but from independent signers to be compressed into one short aggregated signature, which allows verifying all of the signatures simultaneously. In the synchronized setting, the signing algorithm takes the current time step as an additional input. It is assumed that no signer signs more than one message per time step and we aim to aggregate signatures for the same message and same time step. This setting is particularly useful in the context of blockchains, where validators are naturally synchronized by the blocks they sign.

We propose a Weakly Supervised Object Localization (WSOL) method that can locate an object within a given image using a pre-trained network learned with only class labels without location annotations. Most existing WSOL methods rely on thresholding a Class Activation Map (CAM) generated by the pre-trained network to highlight and localize the object. However such approaches often produce incomplete object bounding boxes, as only the discriminative parts of the object are selected during thresholding. We revisit current CAM-based WSOL approaches and propose a pipeline to: (1) refine the CAM map using Weighted Global Average Pooling (WGAP), (2) recombine weights to make use of the negative features, (3) adaptively select a suitable threshold to achieve better object localization. Our method does not require additional learning or hyperparameter tuning. We show that our simple approach can achieve competitive results when evaluated on the CUB-200-2011 and ILSVRC 2016 datasets against other state-of-the-art methods.

Abstract In this paper, we study the hybrid dual attack over learning with errors (LWE) problems for any secret distribution. Prior to our work, hybrid attacks are only considered for sparse and/or small secrets. A new and interesting result from our analysis shows that for most cryptographic use cases a hybrid dual attack outperforms a standalone dual attack, regardless of the secret distribution. We formulate our results into a framework of predicting the performance of the hybrid dual attacks. We also present a few tricks that further improve our attack. To illustrate the effectiveness of our result, we re-evaluate the security of all LWE related proposals in round 3 of NIST’s post-quantum cryptography process, and improve the state-of-the-art cryptanalysis results by 2-15 bits, under the BKZ-core-SVP model.

Vector commitments enable a user to commit to a sequence of values and provably reveal one or many values at specific posi- tions at a later time. In this work, we construct Pointproofs? a new vector commitment scheme that supports non-interactive aggregation of proofs across multiple commitments. Our construction enables any third party to aggregate a collection of proofs with respect to different, independently computed commitments into a single proof represented by an elliptic curve point of 48-bytes. In addition, our scheme is hiding: a commitment and proofs for some values reveal no information about the remaining values. We build Pointproofs and demonstrate how to apply them to blockchain smart contracts. In our example application, Pointproofs reduce bandwidth overheads for propagating a block of transactions by at least 60% compared to prior state- of-art vector commitments. Pointproofs are also efficient: on a single-thread, it takes 0.08 seconds to generate a proof for 8 values with respect to one commitment, 0.25 seconds to aggregate 4000 such proofs across multiple commitments into one proof, and 23 seconds (0.7 ms per value proven) to verify the aggregated proof.

Weakly Supervised Object Localization (WSOL) task aims to recognize the object position by using only image-level labels. Some previous techniques remove the most discriminative parts for all input images or random images to capture the entire object location. However, these methods can not perform the correct operation on different images such as hiding the data or feature maps that should not be hidden. In this case, both classification and localization accuracy will be affected. Meanwhile, just erasing the most important regions tends to make the model learn the less discriminative parts from outside of the objects. To address these limitations, we propose an Attention-based Selection Strategy (ASS) method to choose images that do need to be erased. Moreover, we use different threshold self-attention maps to reduce the impact of unhelpful information in one of the branches of our selection strategy. Based on our experiments, the proposed method is simple but effective to improve the performance of WSOL. In particular, ASS achieves new state-of-the-art accuracy on CUB-200-2011 dataset and works very well on ILSVRC 2016 dataset.

$\sf{ NTRUEncrypt}$is one of the most promising candidates for quantum-safe cryptography. In this paper, we focus on the$\sf{ NTRU743}$parameter set. We give a report on all known attacks against this parameter set and show that it delivers 256 bits of security against classical attackers and 128 bits of security against quantum attackers. We then present a parameter-dependent optimization using a tailored hierarchy of multiplication algorithms as well as the Intel AVX2 instructions, and show that this optimization is constant-time. Our implementation is two to three times faster than the reference implementation of$\sf{ NTRUEncrypt}$.

Vehicular Ad Hoc Network (VANET) allows vehicles to exchange information about road and traffic conditions through wireless communications. Nevertheless, providing reliable and authenticated information without violating the user's privacy seems contradictory. In this paper, we propose an Anonymous Announcement System especially designed for Electric Vehicle (EV) in VANETs to achieve the aforementioned contradictory goals. We demonstrated the feasibility of the protocol with a prototype implementation on a suitable device and a network simulation with our protocol added on top of a normal VANET.

Abstract We propose a circuit extension handshake for Tor that is forward secure against adversaries who gain quantum computing capabilities after session negotiation. In doing so, we refine the notion of an authenticated and confidential channel establishment (ACCE) protocol and define pre-quantum, transitional, and post-quantum ACCE security. These new definitions reflect the types of adversaries that a protocol might be designed to resist. We prove that, with some small modifications, the currently deployed Tor circuit extension handshake, ntor, provides pre-quantum ACCE security. We then prove that our new protocol, when instantiated with a post-quantum key encapsulation mechanism, achieves the stronger notion of transitional ACCE security. Finally, we instantiate our protocol with NTRU-Encrypt and provide a performance comparison between ntor, our proposal, and the recent design of Ghosh and Kate.

All the existing fully homomorphic encryption schemes are based on three different problems, namely the bounded distance decoding problem over ideal lattice, the approximate greatest common divisor problem over integers, and the learning with error problem. In this paper, we unify the first two families of problems by introducing a new class of problems, which can be reduced from both problems. Based on this new problem, namely the bounded distance decoding over hidden ideal lattice, we present a new fully homomorphic encryption scheme. Since it is a combination of the two problems to some extent, the performance of our scheme lies between the ideal lattice based schemes and the integer based schemes. Furthermore, we also show a lower bound and upper bound of the problem that our scheme is based on. Assuming this security conjecture holds, we can incorporate smaller parameters, which will result in a scheme that is more efficient than both lattice based and integer based schemes. Hence, our scheme makes a perfect alternative to the state-of-art ring learning with error based schemes.