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The relay channel, consisting of a source-destination pair and a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel remains unknown, various relaying strategies, including compress-and-forward (CF), have been proposed. For CF, given the correlated signals at the relay and destination, distributed compression techniques, such as Wyner–Ziv coding, can be harnessed to utilize the relay-to-destination link more efficiently. In light of the recent advancements in neural network-based distributed compression, we revisit the relay channel problem, where we integrate a learned one-shot Wyner–Ziv compressor into a primitive relay channel with a finite-capacity and orthogonal (or out-of-band) relay-to-destination link. The resulting neural CF scheme demonstrates that our task-oriented compressor recovers binning of the quantized indices at the relay, mimicking the optimal asymptotic CF strategy, although no structure exploiting the knowledge of source statistics was imposed into the design. We show that the proposed neural CF scheme, employing finite order modulation, operates closely to the capacity of a primitive relay channel that assumes a Gaussian codebook. Our learned compressor provides the first proof-of-concept work toward a practical neural CF relaying scheme. Published in: 2024 IEEE 25th Intern 

Deepfakes have become a dual-use technology with applications in the domains of art, science, and industry. However, the technology can also be leveraged maliciously in areas such as disinformation, identity fraud, and harassment. In response to the technology's dangerous potential many deepfake creation communities have been deplatformed, including the technology's originating community – r/deepfakes. Opening in February 2018, just eight days after the removal of r/deepfakes, MrDeepFakes (MDF) went online as a privately owned platform to fulfill the role of community hub, and has since grown into the largest dedicated deepfake creation and discussion platform currently online. This position of community hub is balanced against the site's other main purpose, which is the hosting of deepfake pornography depicting public figures- produced without consent. In this paper we explore the two largest deepfake communities that have existed via a mixed methods approach utilizing quantitative and qualitative analysis. We seek to identify how these platforms were and are used by their members, what opinions these deepfakers hold about the technology and how it is seen by society at large, and identify how deepfakes-as-disinformation is viewed by the community. We find that there is a large emphasis on technical discussion on these platforms, intermixed with potentially malicious content. Additionally, we find the deplatforming of deepfake communities early in the technology's life has significantly impacted trust regarding alternative community platforms. 

Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where key-gate localities are vulnerable to learning. Thus, we call for security-aware logic synthesis. We propose ALMOST, a framework for adversarial learning to mitigate oracle-less ML attacks via synthesis tuning. ALMOST uses a simulated-annealing-based synthesis recipe generator, employing adversarially trained models that can predict state-of-the-art attacks’ accuracies over wide ranges of recipes and key-gate localities. Experiments on ISCAS benchmarks confirm the attacks’ accuracies drops to around 50% for ALMOST-synthesized circuits, all while not undermining design optimization. 

Automating hardware design could obviate a signif-icant amount of human error from the engineering process and lead to fewer errors. Verilog is a popular hardware description language to model and design digital systems, thus generating Verilog code is a critical first step. Emerging large language models (LLMs) are able to write high-quality code in other programming languages. In this paper, we characterize the ability of LLMs to generate useful Verilog. For this, we fine-tune pre-trained LLMs on Verilog datasets collected from GitHub and Verilog textbooks. We construct an evaluation framework comprising test-benches for functional analysis and a flow to test the syntax of Verilog code generated in response to problems of varying difficulty. Our findings show that across our problem scenarios, the fine-tuning results in LLMs more capable of producing syntactically correct code (25.9% overall). Further, when analyzing functional correctness, a fine-tuned open-source CodeGen LLM can outperform the state-of-the-art commercial Codex LLM (6.5% overall). We release our training/evaluation scripts and LLM checkpoints as open source contributions.