arXiv subjects

Nader Sehatbakhsh

Publications and source records attributed to Nader Sehatbakhsh.

13 recordsLinked to original sources

arXiv2026

Cloak of Invisibility: Real-Time Privacy-Preserving Volumetric Video Streaming

Volumetric video streaming turns privacy into a 3D, multi-view problem. Unlike ordinary video, where sensitive content can often be redacted frame by frame, RGB-D volumetric pipelines capture people, rooms, and personal objects from multiple cameras and fuse them into a shared 3D representation. A private object missed in one view, or only partially removed before fusion, can therefore reappear in the reconstructed scene. This creates a privacy challenge for 3D telepresence, education, entertainment, and immersive applications: private content should be removed before raw visual and geometric data leave the camera side, while the public part of the scene should remain useful for real-time reconstruction. Existing volumetric streaming systems mainly optimize reconstruction, data movement, and latency, while privacy-preserving vision methods are designed for single-camera, single-frame images and do not directly address calibrated multi-view RGB-D fusion. We present InViStream, a real-time "privacy-from-source" system designed for this setting. InViStream addresses three challenges in volumetric capture: private objects may appear differently across views, RGB masking alone can leave geometric privacy leakage in depth, and public/private instances of the same class must be separated consistently before cloud-side fusion. To address these challenges, InViStream combines object detection with depth-aware masking, propagates public/private decisions across calibrated views, and fuses only sanitized point clouds. We evaluate InViStream on synthetic and real RGB-D scenes, including offices, conference rooms, living rooms, and settings with multiple public and private people and objects. InViStream achieves synthetic Dice/Recall of 0.799/0.891 and real Dice/Recall of 0.792/0.908, with synthetic SSIM above 0.98 and real-time streaming above 30 FPS.

cs.CV↗

arXiv2026

GRASP: Reinforcing Language Model Anonymizers with Group Relative Policy Optimization

Large language models can infer sensitive personal attributes, such as age, location, and occupation, from ordinary text, turning everyday writing into a privacy risk. Adversarial anonymization defends against this by rewriting a text with a capable language model that also plays the attacker, but it needs a powerful model at inference time and thus sends private text to a third party, the very exposure anonymization should prevent. Recent work distills this behavior into a small on-device model using supervised fine-tuning and direct preference optimization (DPO), but DPO only imitates the teacher's offline choices and never directly optimizes the privacy--utility objective we care about. We introduce \textbf{GRASP} (\textbf{G}roup-\textbf{R}elative \textbf{A}nonymization via \textbf{S}elf-refinement \textbf{P}olicy-optimization), which reinforces the local anonymizer online with Group Relative Policy Optimization. A single small model acts as anonymizer, adversary, and utility judge, trained against a self-generated reward that hides attributes while preserving meaning, with a design that guards against reward hacking. Trained on Llama-3.1-8B, \ours{} improves the privacy--utility trade-off over the DPO-distilled baseline, consistently across three independent LLM judges. Against adversarial anonymization driven by frontier models such as Gemini~2.5~Flash and Claude, it achieves a comparable or better overall trade-off while removing substantially more private information, and it runs entirely on-device at roughly $1\%$ of the GPT-4o teacher's cost.

cs.CL↗

arXiv2026

SHIFT: Dynamic Compute Relocation Framework for Communication-Aware Chiplet-Based Systems

The increasing communication complexity of large-scale heterogeneous systems has motivated runtime methodologies for communication-aware workload placement and routing optimization. These communication limitations are addressed in this paper by proposing SHIFT, a novel topology-agnostic approach that transfers compute node context and data to a more suitably positioned node, rather than only shifting data as in conventional networks-on-chip. The proposed strategy is evaluated on a chiplet-based architecture utilizing a fine-pitch integration platform featuring multiple bandwidth-domains for heterogeneous workloads. The proposed architecture employs multi-layered routing between functional or memory chiplets and utility chiplets, which serve as intelligent nodes for routing and compute relocation. Adaptive scheduling and routing utilize a modified shortest-path algorithm for large-scale systems, complemented by a lightweight ML-assisted policy that infers traffic conditions to improve adaptivity. To establish a performance baseline, the initial assessment uses random instruction vectors and data patterns to evaluate the fundamental capabilities of SHIFT. Simulation results exhibit successful relocations over total trials ranging from 75.2% to 97.9% across configurations, with average latency improvements of 16.4%-62.5% and a maximum of 76.8%. In addition, throughput is improved by up to 12.5x, power dissipation per unit area is reduced by ~8%, energy-per-bit is reduced by up to 58.3%, and performance is improved by 18%. To evaluate efficiency under high logic and data density, the framework was tested on standard LLM workloads. Results exhibit average improvements of 4.9x, 5.9x, and 1.8x in runtime, throughput, and energy-efficiency, respectively, surpassing state-of-the-art wafer-scale LLM services and demonstrating compatibility with large-scale platforms and applications.

cs.AR↗

arXiv2026

Defense Against Prompt Inversion Attacks: An Information-Theoretic Approach for LLM Collaborative Inference

Collaborative edge-cloud inference enables resource-constrained devices to leverage large language models (LLMs) by offloading partial computation to cloud servers. However, transmitting intermediate activations exposes sensitive user prompts to prompt inversion attacks, where an adversary reconstructs the original input from shared representations. Existing defenses rely largely on heuristic perturbations or empirical tuning, offering limited theoretical understanding of privacy leakage and its interaction with utility and latency constraints. We propose an information-theoretic defense framework for prompt inversion in collaborative LLM inference. Our approach learns privacy-preserving representations by explicitly minimizing the mutual information between intermediate activations and the input prompt while maintaining task utility under computational constraints. We derive theoretical guarantees on prompt reconstruction error, characterize fundamental privacy-utility tradeoffs, and establish token-level accuracy bounds for downstream inference. We then propose a novel defense based on privacy adapters implemented via low-dimensional information bottlenecks. Extensive experiments across multiple settings demonstrate that our method achieves superior privacy-utility-latency tradeoffs compared to existing defenses (up to 35% reduction in attack success), providing a principled foundation for private and efficient collaborative LLM inference.

cs.CR↗

arXiv2026

ProbeAct: Probe-Guided Training-Free Failure Recovery in Vision-Language-Action Models

Vision-Language-Action (VLA) models demonstrate strong perfor-1 mance on language-conditioned robotic manipulation within their training dis-2 tribution, yet their generalization capabilities remain fundamentally limited. They3 lack the robustness required to handle perturbations, frequently failing when con-4 fronted with lighting changes, altered camera viewpoints, or small initial-state5 variations. We propose PROBEACT, a training-free runtime intervention frame-6 work that detects and recovers from grasping and placement failures in pre-7 trained VLA policies without modifying their weights or requiring additional8 demonstrations. PROBEACT combines three components: (i) a lightweight multi-9 target hidden-state probe that predicts the 3D positions of task-relevant objects10 from intermediate VLA features, with Hungarian-matched identity tracking for11 multi-object scenes; (ii) an object-agnostic kinematic state machine that detects12 grasp, transport, and placement failures using only gripper-internal signals and13 end-effector kinematics; and (iii) a hierarchical Control Barrier Function (CBF)14 filter that encodes repeated-failure locations as soft safe-set constraints, mini-15 mally correcting VLA actions while preserving baseline behavior. As a plug-and-16 play, training-free intervention loop, PROBEACT is orthogonal to existing train-17 ing pipelines. Evaluated on the LIBERO-plus benchmark, our framework acts as18 a universal safety net, improving the success rate of the OpenVLA-OFT model19 from 69.6% to 74.1%, while demonstrating broad applicability to both base and20 fine-tuned VLA policies.

cs.RO↗

arXiv2026

Your Model Already Knows: Attention-Guided Safety Filter for Vision-Language-Action Models

Vision-Language-Action (VLA) models have demonstrated impressive end-to-end performance across a variety of robotic manipulation tasks. However, these policies offer no guarantees against collisions with task-irrelevant objects in the scene. Existing safety filters sidestep this problem by querying a vision-language model (VLM) to identify obstacles and their locations. This, however, is too slow to run in the control loop and can only be invoked at episode initialization, leaving the filter unable to track moving obstacles. We discover that a small number of attention heads within a VLA model reliably localize the object the policy intends to approach. These heads can be exploited within a training-free safety framework that obtains the active target from the attention heads at every step, treats the remainder of the scene as obstacles, and feeds these into a Control Barrier Function (CBF) filter. Together with a lightweight real-time object tracker, this allows for collision avoidance for non-static obstacles. We evaluate our framework on SafeLIBERO, which we extend with moving obstacles. On the original static benchmark, our method performs comparably to an oracle that uses privileged simulator state to identify the target, emulating a VLM-based identification step run once at episode initialization. On the dynamic variant, where the oracle's init-time target assignment becomes stale, our method substantially outperforms it by 43%, on average. Our findings suggest that the perceptual signals needed for real-time safety filtering are already present within VLA policies and can be exploited without additional training or heavy auxiliary models.

cs.RO↗

arXiv2026

The Internet of Collaborating Things: Agentic Edge AI for Autonomous Cross-Domain Collaboration

The Internet of Things is on a trajectory toward a trillion connected devices deployed across multiple domains. These devices are no longer simple sensing and actuation endpoints; they are mobile platforms with embedded processing and intelligent on-device services. The dominant paradigm of offloading computation to cloud and edge servers is a vertical device-to-server interaction model that cannot scale with this trajectory. What is needed instead is a horizontal paradigm in which devices communicate and collaborate directly, pooling their compute, sensing, and actuation into dynamic, cross-domain clusters rather than offloading to centralized infrastructure. We refer to this paradigm as the Internet of Collaborating Things (IoCT). Realizing this vision requires both a portable and secure execution substrate for heterogeneous hardware and an agentic control plane capable of contextual reasoning, transient trust establishment, and open-world adaptation throughout the device collaboration lifecycle without human intervention. In this position paper, we define this new communication, compute, and collaboration paradigm, articulate the role of agentic edge AI in realizing it, and outline research challenges and future directions toward trustworthy, autonomous IoCT collaboration.

cs.DC↗

arXiv2025

SuperPure: Efficient Purification of Localized and Distributed Adversarial Patches via Super-Resolution GAN Models

As vision-based machine learning models are increasingly integrated into autonomous and cyber-physical systems, concerns about (physical) adversarial patch attacks are growing. While state-of-the-art defenses can achieve certified robustness with minimal impact on utility against highly-concentrated localized patch attacks, they fall short in two important areas: (i) State-of-the-art methods are vulnerable to low-noise distributed patches where perturbations are subtly dispersed to evade detection or masking, as shown recently by the DorPatch attack; (ii) Achieving high robustness with state-of-the-art methods is extremely time and resource-consuming, rendering them impractical for latency-sensitive applications in many cyber-physical systems. To address both robustness and latency issues, this paper proposes a new defense strategy for adversarial patch attacks called SuperPure. The key novelty is developing a pixel-wise masking scheme that is robust against both distributed and localized patches. The masking involves leveraging a GAN-based super-resolution scheme to gradually purify the image from adversarial patches. Our extensive evaluations using ImageNet and two standard classifiers, ResNet and EfficientNet, show that SuperPure advances the state-of-the-art in three major directions: (i) it improves the robustness against conventional localized patches by more than 20%, on average, while also improving top-1 clean accuracy by almost 10%; (ii) It achieves 58% robustness against distributed patch attacks (as opposed to 0% in state-of-the-art method, PatchCleanser); (iii) It decreases the defense end-to-end latency by over 98% compared to PatchCleanser. Our further analysis shows that SuperPure is robust against white-box attacks and different patch sizes. Our code is open-source.

cs.CV↗

arXiv2025

To See or Not to See -- Fingerprinting Devices in Adversarial Environments Amid Advanced Machine Learning

The increasing use of the Internet of Things raises security concerns. To address this, device fingerprinting is often employed to authenticate devices, detect adversaries, and identify eavesdroppers in an environment. This requires the ability to discern between legitimate and malicious devices which is achieved by analyzing the unique physical and/or operational characteristics of IoT devices. In the era of the latest progress in machine learning, particularly generative models, it is crucial to methodically examine the current studies in device fingerprinting. This involves explaining their approaches and underscoring their limitations when faced with adversaries armed with these ML tools. To systematically analyze existing methods, we propose a generic, yet simplified, model for device fingerprinting. Additionally, we thoroughly investigate existing methods to authenticate devices and detect eavesdropping, using our proposed model. We further study trends and similarities between works in authentication and eavesdropping detection and present the existing threats and attacks in these domains. Finally, we discuss future directions in fingerprinting based on these trends to develop more secure IoT fingerprinting schemes.

cs.CR↗

arXiv2024

LightPure: Realtime Adversarial Image Purification for Mobile Devices Using Diffusion Models

Autonomous mobile systems increasingly rely on deep neural networks for perception and decision-making. While effective, these systems are vulnerable to adversarial machine learning attacks where minor input perturbations can significantly impact outcomes. Common countermeasures involve adversarial training and/or data or network transformation. These methods, though effective, require full access to typically proprietary classifiers and are costly for large models. Recent solutions propose purification models, which add a "purification" layer before classification, eliminating the need to modify the classifier directly. Despite their effectiveness, these methods are compute-intensive, making them unsuitable for mobile systems where resources are limited and low latency is essential. This paper introduces LightPure, a new method that enhances adversarial image purification. It improves the accuracy of existing purification methods and provides notable enhancements in speed and computational efficiency, making it suitable for mobile devices with limited resources. Our approach uses a two-step diffusion and one-shot Generative Adversarial Network (GAN) framework, prioritizing latency without compromising robustness. We propose several new techniques to achieve a reasonable balance between classification accuracy and adversarial robustness while maintaining desired latency. We design and implement a proof-of-concept on a Jetson Nano board and evaluate our method using various attack scenarios and datasets. Our results show that LightPure can outperform existing methods by up to 10x in terms of latency while achieving higher accuracy and robustness for various attack scenarios. This method offers a scalable and effective solution for real-world mobile systems.

cs.CR↗

arXiv2024

Solutions to Deepfakes: Can Camera Hardware, Cryptography, and Deep Learning Verify Real Images?

The exponential progress in generative AI poses serious implications for the credibility of all real images and videos. There will exist a point in the future where 1) digital content produced by generative AI will be indistinguishable from those created by cameras, 2) high-quality generative algorithms will be accessible to anyone, and 3) the ratio of all synthetic to real images will be large. It is imperative to establish methods that can separate real data from synthetic data with high confidence. We define real images as those that were produced by the camera hardware, capturing a real-world scene. Any synthetic generation of an image or alteration of a real image through generative AI or computer graphics techniques is labeled as a synthetic image. To this end, this document aims to: present known strategies in detection and cryptography that can be employed to verify which images are real, weight the strengths and weaknesses of these strategies, and suggest additional improvements to alleviate shortcomings.

cs.CV↗

arXiv2021

SoK: A Study of the Security on Voice Processing Systems

As the use of Voice Processing Systems (VPS) continues to become more prevalent in our daily lives through the increased reliance on applications such as commercial voice recognition devices as well as major text-to-speech software, the attacks on these systems are increasingly complex, varied, and constantly evolving. With the use cases for VPS rapidly growing into new spaces and purposes, the potential consequences regarding privacy are increasingly more dangerous. In addition, the growing number and increased practicality of over-the-air attacks have made system failures much more probable. In this paper, we will identify and classify an arrangement of unique attacks on voice processing systems. Over the years research has been moving from specialized, untargeted attacks that result in the malfunction of systems and the denial of services to more general, targeted attacks that can force an outcome controlled by an adversary. The current and most frequently used machine learning systems and deep neural networks, which are at the core of modern voice processing systems, were built with a focus on performance and scalability rather than security. Therefore, it is critical for us to reassess the developing voice processing landscape and to identify the state of current attacks and defenses so that we may suggest future developments and theoretical improvements.

cs.CR↗

arXiv2020

Security and Privacy Considerations for Machine Learning Models Deployed in the Government and Public Sector (white paper)

As machine learning becomes a more mainstream technology, the objective for governments and public sectors is to harness the power of machine learning to advance their mission by revolutionizing public services. Motivational government use cases require special considerations for implementation given the significance of the services they provide. Not only will these applications be deployed in a potentially hostile environment that necessitates protective mechanisms, but they are also subject to government transparency and accountability initiatives which further complicates such protections. In this paper, we describe how the inevitable interactions between a user of unknown trustworthiness and the machine learning models, deployed in governments and public sectors, can jeopardize the system in two major ways: by compromising the integrity or by violating the privacy. We then briefly overview the possible attacks and defense scenarios, and finally, propose recommendations and guidelines that once considered can enhance the security and privacy of the provided services.

cs.CR↗