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However, since the proposal of APGD, it has been difficult for such methods to achieve significant breakthroughs. To achieve such an effect, we first analyze the issue of \"high-loss non-adversarial examples\" that degrades attack performance in previous methods, and prove that this issue arises from inappropriate objectives for adversarial example generation. Subsequently, we reconstruct the objective as \"maximizing the difference between the non-ground-truth label probability upper bound and the ground-truth label probability\", and proposes a novel and powerful gradient-based attack method named Sequential Difference Maximization (SDM). SDM establishes a three-layer optimization framework of \"cycle-stage-step\". It adopts the negative probability loss function and the Directional Probability Difference Ratio (DPDR) loss function in the initial and subsequent o","title":"SDM: A Powerful Tool for Evaluating Model Robustness","url":"https://arxiv.org/abs/2605.20308","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.20308v1 Announce Type: cross \nAbstract: Gradient-based attacks are important methods for evaluating model robustness. However, since the proposal of APGD, it has been difficult for such methods to achieve significant breakthroughs. To achieve such an effect, we first analyze the issue of \"high-loss non-adversarial examples\" that degrades attack performance in previous methods, and prove that this issue arises from inappropriate objectives for adversarial example generation. Subsequently, we reconstruct the objective as \"maximizing the difference between the non-ground-truth label probability upper bound and the ground-truth label probability\", and proposes a novel and powerful gradient-based attack method named Sequential Difference Maximization (SDM). SDM establishes a three-layer optimization framework of \"cycle-stage-step\". It adopts the negative probability loss function and the Directional Probability Difference Ratio (DPDR) loss function in the initial and subsequent o","title":"SDM: A Powerful Tool for Evaluating Model Robustness","vendor":"arxiv_cs_ai"},"confidence":{"method":"deterministic"},"decision":"POSITIVE","issued_at":"2026-05-22T04:43:12Z","notes":"Spider vendor_press (news) news.vendor_press.v1","object":{"captured_by":"crovia.spider.vendor_press","primary_source_url":"https://arxiv.org/abs/2605.20308"},"predecessors":[],"schema":"crovia.axiom.v1","signature":"ed25519:9d8b4ecb2a77a734709adea5a80b95974e7e110aa0fa33475577a856118c54604f0d76b8f8367b759ed764309811154d636f08d12eb5b789b577f5e3e7ba0008","signer":"crovia.substrate","subject":{"observed_at":"2026-05-22T04:43:12Z","source_collector":"spider:vendor_press","target_id":"https://arxiv.org/abs/2605.20308"},"tsa":{"authority":"crovia.substrate.bootstrap","rfc3161_token":"{\"kind\":\"crovia.bootstrap.tsa\",\"source_jsonl\":\"/opt/crovia/spider/data/news/vendor_press_v1.jsonl\",\"source_seal_merkle_root\":\"spider_vendor_press_v1\",\"upgrade_path\":\"Sessione H \\u2014 OpenTimestamps weekly anchor\"}"},"zk_mode":"clear","zk_proof":null},"ledger":{"leaf_hash":"26cdfc5a896f12c2d71617010566370117162702c0384d07506b8a63e226d419","leaf_index":148205,"ledger_path":"/opt/crovia/substrate/axiom_ledger.jsonl"},"merkle_proof":{"hash_alg":"sha256","leaf_prefix":"0x00","node_prefix":"0x01","odd_leaf_rule":"duplicate_last","path":[{"sibling":"e391b8eb0bc0b662e1385a47afae4edae00386b2cb77d068b62a52c91cd61e22","side":"left"},{"sibling":"c4a05b564603a9210684ffbb4afadd29faaaef2236cf2d42ba2903f48908322c","side":"right"},{"sibling":"68adaff65a8680cb64f440dd4098ce85f997c5f0f4f903a843a4c1f36c778fbc","side":"left"},{"sibling":"6757acf93ea8e3c11adb6c2851b5d997086a28b262af01df568d744319df07dc","side":"left"},{"sibling":"1c7ab905eb4d289a16032228eaa2d4fc70c9c85fe0822b11312c184dad3a95f0","side":"right"},{"sibling":"9c8ee5d5ee587378292cc512af6677d5e68736fc250e3e729ba4a22e4dd9c55f","side":"left"},{"sibling":"9041a5a271687216ad8ad42ade0fd8c711a35ed68eb350b81aed4384188eec3c","side":"left"},{"sibling":"ce8423e7b33fd98ad2188ec515d860afebe3ce0e74717c41376c90ea6acfb384","side":"left"},{"sibling":"c5d582bc1cdd6d6494fd9e29c8b4aadd02d777f7ef92fc6d4afad7ee42785e79","side":"right"},{"sibling":"d337a9fdcfc121e9691d8db9173af6a3fe0c33d4a6d5f0c8a7a01af04f9fb856","side":"left"},{"sibling":"8b39e07457f5cc5d687d2ae42284dbe705bb87084e7db4b626aff81e51dacd19","side":"right"},{"sibling":"79a713e1e345ccb99c5fe994a11708c8e9bcfa2e91f940d70421cb7d8d77ecc6","side":"right"},{"sibling":"249870fb494bef050c409081e5de45f9042938d7b2823524ee496f296aa63667","side":"right"},{"sibling":"96c48ee8328f1b7925a4cc4421df5cb0bd81c92a5d8354c93126fa5f0166d225","side":"right"},{"sibling":"35ca36cee447f0ef7064a25d55f59357c66901e31427729c4c1d8b14aa8adb6c","side":"left"},{"sibling":"4e13b4a3e69bb83d13913477a782913ce03937edd65046853c1964d3cbb6564b","side":"right"},{"sibling":"0f7b2df1c4580bf7bb7c24b9158ba20593a06af18a5f18d0973e5eff20c35cd8","side":"right"},{"sibling":"d841ad93efda0869e5eb97678f348f03f5caab4353e05ff4bf18f47fb945b822","side":"left"}]},"schema":"crovia.axiom_proof.v1","seal":{"first_collector_run_id":"","first_receipt_hash":"","jsonl_path":"/opt/crovia/substrate/axiom_ledger.jsonl","key_id":"430895f101d38164","last_collector_run_id":"","last_receipt_hash":"","leaf_count":148601,"merkle_root":"44900cffd986f40535c83f46a250e86fbf1019d41f27080a00fbf9b8d77ec33a","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","run_id":"hourly_json_retrofit_20260524T133701Z","schema":"crovia.seal.v1","seal_family_version":"crovia-seal-family/1","seal_kind":"substrate_batch","sealed_at":"2026-05-24T13:37:32Z","sig_algorithm":"ed25519","signature":"059e428c3c5241de303721ad6ac7b748758372180f3f0a717810312aecd6fab073abb3ea264157666de581a2b361c4c6e2aa8ca081b08ce9be090721f0e3400e","signer_version":"1.1.0"},"trust_root":{"key_id":"430895f101d38164","public_key_hex":"cf742e26f75669dc673cb5c0786a1ae23ae8ca19c347317192ce40c28a7ff25c","signature_algorithm":"ed25519","url":"/registry/canon/TRUST_ROOT.md"},"verifier":{"spec":"/registry/canon/AXIOM_RECEIPT_v1.md","url":"/v/axm_3d8a63b29589266693fcab9524011d9fa471e08fc61c032d1f0c121c2ce4dc14"}}