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Open problems in Adversarial training

Robust adversarial detection with theoretical guarantees3 papers raise it · 3 address it
Current detectors rely on heuristics and fail under adaptive attacks, leaving models vulnerable to adversarial inputs.
Emergent misalignment in multimodal models1 paper raises it · 1 addresses it
Fine-tuning on narrow tasks can induce broadly harmful behaviors in vision-language models, but its mechanisms and mitigation are poorly understood.
- Narrow Multimodal Fine-Tuning Can Induce Emergent Misalignmentraises · addresses
Safe and high-quality generation trade-off1 paper raises it · 1 addresses it
Restricting training data for safety reduces model quality, while post-hoc unlearning risks catastrophic forgetting.
- Pristine Modelsraises · addresses
Robustness of decision models under adversarial manipulation1 paper raises it · 1 addresses it
Decision models are increasingly used for automated tasks, but their reliability under attacks is untested and unstandardized.
- DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Visionraises · addresses
Watermark removal resilience1 paper raises it · 2 address it
Invisible watermarks for ownership verification are vulnerable to simple, training-free attacks that preserve image quality.
- BrushOff: Stroke-Based Re-rendering as a Watermark Removal Attack on Paintingraises · addresses
- SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verificationaddresses

Named by an AI from these papers' abstracts.

Open problems

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Topics in this field

deepfake detection · forensic cues · image forgery107 papers21 this week
adversarial training · adversarial attacks · adversarial images107 papers15 this week
watermarking schemes · watermarking · based watermarking76 papers12 this week

Topics in this field

Active contributors · 90 days

Active contributors

Activity · 12 weeks

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Activity

Open problems in Adversarial training

Robust adversarial detection with theoretical guarantees3 papers raise it · 3 address it
Current detectors rely on heuristics and fail under adaptive attacks, leaving models vulnerable to adversarial inputs.
Emergent misalignment in multimodal models1 paper raises it · 1 addresses it
Fine-tuning on narrow tasks can induce broadly harmful behaviors in vision-language models, but its mechanisms and mitigation are poorly understood.
- Narrow Multimodal Fine-Tuning Can Induce Emergent Misalignmentraises · addresses
Safe and high-quality generation trade-off1 paper raises it · 1 addresses it
Restricting training data for safety reduces model quality, while post-hoc unlearning risks catastrophic forgetting.
- Pristine Modelsraises · addresses
Robustness of decision models under adversarial manipulation1 paper raises it · 1 addresses it
Decision models are increasingly used for automated tasks, but their reliability under attacks is untested and unstandardized.
- DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Visionraises · addresses
Watermark removal resilience1 paper raises it · 2 address it
Invisible watermarks for ownership verification are vulnerable to simple, training-free attacks that preserve image quality.
- BrushOff: Stroke-Based Re-rendering as a Watermark Removal Attack on Paintingraises · addresses
- SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verificationaddresses

Named by an AI from these papers' abstracts.

Activity · 12 weeks

Coming with more sources: repos

Recent papers

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arXiv cs.CVOct 16
Reliability-Consistent Appearance Modeling for RGB-Infrared Multi-Object Tracking
Bingyang Wang, Jiazuo Yu, Lu Zhang, Lihe Zhang, Huchuan Lu +1
Recent RGB-infrared multi-object tracking (MOT) methods have substantially improved cross-modal representation learning and multimodal information integration. However, most existing methods compress RGB and infrared evidence into a unified descriptor. When one modality is temporarily degraded, the tracker can no longer regulate its contribution separately; unreliable evidence may distort association and contaminate appearance memory. We therefore propose RCA-MOT, a reliability-consistent appearance modeling framework that preserves cross-modal complementarity while retaining post-representation evidence controllability. RGB-specific, infrared-specific, and fused identity representations are maintained as jointly accessible appearance states. For each detection, a simple training-free target-scene estimator produces branch-wise appearance reliability used across the two temporal roles of appearance: relative reliability regulates different identity branches, absolute reliability calibrates borderline candidates near the ReID acceptance boundary, and the same branch-wise reliability controls how strongly matched descriptors update historical appearance memory. The individual…
PDF arXiv
arXiv cs.CLOct 16
Watermarkability: Measuring and Improving Language-Model Compatibility with Logit-Based Watermarks
Zewei Deng, Mohamed Seif, Liyan Xie
Language-model watermarks can help identify model-generated text, encode provenance information, and detect post-generation edits. A broad class of logit-based watermarks increases the logits of a designated token set at each decoding step. Because a base model's native probability mass on this set varies across contexts, models, and watermark rules, the same logit bias can yield different adherence rates and increases in perplexity (PPL). We introduce watermarkability to characterize the compatibility between a model and the token sets requested by a watermark. At a target mass $τ$, we quantify this property through a watermarkability cost: the expected minimum KL divergence from the base-model distribution required to assign probability at least $τ$ to the requested set. A lower cost means that the model is more watermarkable for that request distribution. To reduce this cost, we propose a two-stage method. In the first stage, we train a target-set-conditioned low-rank adapter on unions drawn from a 16-way vocabulary partition to increase the requested set's probability mass. In the second stage, a closed-form controller applies the smallest remaining uniform logit bias needed…
PDF arXiv
arXiv cs.CVOct 15
Cross-Person Identity Change Under Source-Coupled Multi-Human Generation: A Controlled UniPortrait Study
Edrees Al-Edreesi, Ghaleb Al-Gaphari, Muneer Alsurori, Mohammed Edrees, Fouad Al-Hamdani
Multi-human personalization must account for identity changes in people outside the nominal target of a generative operation. We characterize this behavior in UniPortrait, without introducing a new preservation algorithm. Two sequential studies use 119 accepted two-person baseline scenes and three fixed output seeds, measuring continuous reference-based facial similarity with InsightFace \buffalo. In Study~1, source-coupled single-ID target-only generation (A1) produces negative mean transitions for both the protected person ($-0.567904$) and target ($-0.236904$), indicating broad perturbation rather than established successful repair with collateral loss. In Study~2, native dual-ID generation (A2) yields more positive paired contrasts: protected $+0.512918$ (95\% case-cluster CI $[0.471434,0.550259]$) and target $+0.158853$ ($[0.118903,0.197523]$), across 287 jointly measurable pairs from 111 cases. Shared baseline similarity cancels algebraically in these paired contrasts, leaving a matched POST comparison. Validation designed after the primary result freeze finds directional agreement with an independent identity evaluator and blinded human judgments; within-\buffalo\ nuisance…
PDF arXiv
arXiv cs.CVOct 15
Deep Learning for Deepfake Image Detection: Comparative Model Evaluation and Gradient-Based Visual Attribution
Saad Korchi, Abderrahim Waga
Synthetic facial imagery has made the distinction between authentic and generated content increasingly important for digital forensics and online trust. This study examines an explainable deep-learning workflow for image-level deepfake detection using two contrasting classification strategies: a compact convolutional model trained from scratch and an ImageNet-pretrained MobileNetV2 model adapted through two-stage transfer learning. Experiments were conducted with the balanced 140k Real and Fake Faces dataset, comprising 140,000 facial images. Under the recorded validation protocol, the augmented from-scratch model reached 84.15% accuracy (loss 0.3625), compared with 95.90% accuracy (loss 0.1074) for the fine-tuned pretrained model. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize image regions associated with predictions, and a Flask-based prototype was developed for side-by-side inspection. These observations support the practical value of pretrained visual representations in the studied setting while underscoring the need to interpret validation performance alongside dataset provenance, explanation limitations, and evaluation scope. The reported…
PDF arXiv
arXiv cs.SDOct 15
Backward Compatibility in Zero-Bit Neural Audio Watermarking
Admir Krnić, Kosta Pavlović, Petar Nedić, Željko Bolević, Slavko Kovačević +1
Neural watermarking systems are typically trained as tightly coupled embedder-detector pairs and implicitly assume static deployment. In real-world deployments, embedders and detectors are frequently updated and selectively decoupled for security and versioning purposes, which can break detection in previously embedded content and, more critically, increase false-positive rates on embedders that a given detector is intentionally not intended to recognize. In this paper the concept of backward compatibility in zero-bit audio watermarking is formalized. A training framework is proposed that pairs a new detector with a sequence of frozen legacy embedders (its legacy chain), using a performance-weighted sampling strategy so that the detector retains accuracy across all earlier versions. This coupling, while necessary for backward compatibility, is shown to induce an embedder-collapse effect that catastrophically degrades the true negative rate (TNR) against out-of-chain embedders. To counteract this, a Angular Diversity Loss (ADL) is introduced that explicitly forces each new embedder to occupy a distinct region of the output space. The combined approach restores near-perfect TNR…
PDF arXiv
arXiv cs.CROct 15
Erase the Mark, Preserve the Motion: Temporally Consistent Removal of Video Watermarks
Yifan Liao, Zhen Sun, Zhicong Huang, Jiaming He, Zongmin Zhang +4
Recent advances in video generation have increased the demand for reliable watermarking for copyright protection, yet the robustness of video watermarks against removal attacks remains underexplored. Most watermark-removal methods originate from the image domain: signal optimization injects imperceptible pixel-domain perturbations, whereas generative resynthesis produces a visually similar instance through diffusion synthesis. When extended to videos, the former introduces temporally varying noise that causes visible flickering, while the latter regenerates fine-grained structures inconsistently across frames, leading to structural motion inconsistency. To evaluate video watermark robustness, we propose PoseRegen, a pose-consistent reconstruction framework that removes watermarks while preserving the original content and motion. PoseRegen first applies a temporally shared viewpoint transformation to disrupt watermark-bearing spatial patterns and then jointly reconstructs the transformed sequence with a pose-conditioned video model. A frozen video DiT processes the complete sequence with its reference-video latent, and full-sequence Flow Matching supervision keeps identity and…
PDF arXiv

Papers

Topics in this field

deepfake detection · forensic cues · image forgery107 papers21 this week
adversarial training · adversarial attacks · adversarial images107 papers15 this week
watermarking schemes · watermarking · based watermarking76 papers12 this week

Active contributors · 90 days

Recent papers

All in Research →

arXiv cs.CVOct 16
Reliability-Consistent Appearance Modeling for RGB-Infrared Multi-Object Tracking
Bingyang Wang, Jiazuo Yu, Lu Zhang, Lihe Zhang, Huchuan Lu +1
Recent RGB-infrared multi-object tracking (MOT) methods have substantially improved cross-modal representation learning and multimodal information integration. However, most existing methods compress RGB and infrared evidence into a unified descriptor. When one modality is temporarily degraded, the tracker can no longer regulate its contribution separately; unreliable evidence may distort association and contaminate appearance memory. We therefore propose RCA-MOT, a reliability-consistent appearance modeling framework that preserves cross-modal complementarity while retaining post-representation evidence controllability. RGB-specific, infrared-specific, and fused identity representations are maintained as jointly accessible appearance states. For each detection, a simple training-free target-scene estimator produces branch-wise appearance reliability used across the two temporal roles of appearance: relative reliability regulates different identity branches, absolute reliability calibrates borderline candidates near the ReID acceptance boundary, and the same branch-wise reliability controls how strongly matched descriptors update historical appearance memory. The individual…
PDF arXiv
arXiv cs.CLOct 16
Watermarkability: Measuring and Improving Language-Model Compatibility with Logit-Based Watermarks
Zewei Deng, Mohamed Seif, Liyan Xie
Language-model watermarks can help identify model-generated text, encode provenance information, and detect post-generation edits. A broad class of logit-based watermarks increases the logits of a designated token set at each decoding step. Because a base model's native probability mass on this set varies across contexts, models, and watermark rules, the same logit bias can yield different adherence rates and increases in perplexity (PPL). We introduce watermarkability to characterize the compatibility between a model and the token sets requested by a watermark. At a target mass $τ$, we quantify this property through a watermarkability cost: the expected minimum KL divergence from the base-model distribution required to assign probability at least $τ$ to the requested set. A lower cost means that the model is more watermarkable for that request distribution. To reduce this cost, we propose a two-stage method. In the first stage, we train a target-set-conditioned low-rank adapter on unions drawn from a 16-way vocabulary partition to increase the requested set's probability mass. In the second stage, a closed-form controller applies the smallest remaining uniform logit bias needed…
PDF arXiv
arXiv cs.CVOct 15
Cross-Person Identity Change Under Source-Coupled Multi-Human Generation: A Controlled UniPortrait Study
Edrees Al-Edreesi, Ghaleb Al-Gaphari, Muneer Alsurori, Mohammed Edrees, Fouad Al-Hamdani
Multi-human personalization must account for identity changes in people outside the nominal target of a generative operation. We characterize this behavior in UniPortrait, without introducing a new preservation algorithm. Two sequential studies use 119 accepted two-person baseline scenes and three fixed output seeds, measuring continuous reference-based facial similarity with InsightFace \buffalo. In Study~1, source-coupled single-ID target-only generation (A1) produces negative mean transitions for both the protected person ($-0.567904$) and target ($-0.236904$), indicating broad perturbation rather than established successful repair with collateral loss. In Study~2, native dual-ID generation (A2) yields more positive paired contrasts: protected $+0.512918$ (95\% case-cluster CI $[0.471434,0.550259]$) and target $+0.158853$ ($[0.118903,0.197523]$), across 287 jointly measurable pairs from 111 cases. Shared baseline similarity cancels algebraically in these paired contrasts, leaving a matched POST comparison. Validation designed after the primary result freeze finds directional agreement with an independent identity evaluator and blinded human judgments; within-\buffalo\ nuisance…
PDF arXiv
arXiv cs.CVOct 15
Deep Learning for Deepfake Image Detection: Comparative Model Evaluation and Gradient-Based Visual Attribution
Saad Korchi, Abderrahim Waga
Synthetic facial imagery has made the distinction between authentic and generated content increasingly important for digital forensics and online trust. This study examines an explainable deep-learning workflow for image-level deepfake detection using two contrasting classification strategies: a compact convolutional model trained from scratch and an ImageNet-pretrained MobileNetV2 model adapted through two-stage transfer learning. Experiments were conducted with the balanced 140k Real and Fake Faces dataset, comprising 140,000 facial images. Under the recorded validation protocol, the augmented from-scratch model reached 84.15% accuracy (loss 0.3625), compared with 95.90% accuracy (loss 0.1074) for the fine-tuned pretrained model. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize image regions associated with predictions, and a Flask-based prototype was developed for side-by-side inspection. These observations support the practical value of pretrained visual representations in the studied setting while underscoring the need to interpret validation performance alongside dataset provenance, explanation limitations, and evaluation scope. The reported…
PDF arXiv
arXiv cs.SDOct 15
Backward Compatibility in Zero-Bit Neural Audio Watermarking
Admir Krnić, Kosta Pavlović, Petar Nedić, Željko Bolević, Slavko Kovačević +1
Neural watermarking systems are typically trained as tightly coupled embedder-detector pairs and implicitly assume static deployment. In real-world deployments, embedders and detectors are frequently updated and selectively decoupled for security and versioning purposes, which can break detection in previously embedded content and, more critically, increase false-positive rates on embedders that a given detector is intentionally not intended to recognize. In this paper the concept of backward compatibility in zero-bit audio watermarking is formalized. A training framework is proposed that pairs a new detector with a sequence of frozen legacy embedders (its legacy chain), using a performance-weighted sampling strategy so that the detector retains accuracy across all earlier versions. This coupling, while necessary for backward compatibility, is shown to induce an embedder-collapse effect that catastrophically degrades the true negative rate (TNR) against out-of-chain embedders. To counteract this, a Angular Diversity Loss (ADL) is introduced that explicitly forces each new embedder to occupy a distinct region of the output space. The combined approach restores near-perfect TNR…
PDF arXiv
arXiv cs.CROct 15
Erase the Mark, Preserve the Motion: Temporally Consistent Removal of Video Watermarks
Yifan Liao, Zhen Sun, Zhicong Huang, Jiaming He, Zongmin Zhang +4
Recent advances in video generation have increased the demand for reliable watermarking for copyright protection, yet the robustness of video watermarks against removal attacks remains underexplored. Most watermark-removal methods originate from the image domain: signal optimization injects imperceptible pixel-domain perturbations, whereas generative resynthesis produces a visually similar instance through diffusion synthesis. When extended to videos, the former introduces temporally varying noise that causes visible flickering, while the latter regenerates fine-grained structures inconsistently across frames, leading to structural motion inconsistency. To evaluate video watermark robustness, we propose PoseRegen, a pose-consistent reconstruction framework that removes watermarks while preserving the original content and motion. PoseRegen first applies a temporally shared viewpoint transformation to disrupt watermark-bearing spatial patterns and then jointly reconstructs the transformed sequence with a pose-conditioned video model. A frozen video DiT processes the complete sequence with its reference-video latent, and full-sequence Flow Matching supervision keeps identity and…
PDF arXiv

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